Distribution and Phylogeny of EFL and EF-1α in Euglenozoa Suggest Ancestral Co-Occurrence Followed by Differential Loss

BACKGROUND: The eukaryotic elongation factor EF-1alpha (also known as EF1A) catalyzes aminoacyl-tRNA binding by the ribosome during translation. Homologs of this essential protein occur in all domains of life, and it was previously thought to be ubiquitous in eukaryotes. Recently, however, a number of eukaryotes were found to lack EF-1alpha and instead encode a related protein called EFL (for EF-Like). EFL-encoding organisms are scattered widely across the tree of eukaryotes, and all have close relatives that encode EF-1alpha. This intriguingly complex distribution has been attributed to multiple lateral transfers because EFL's near mutual exclusivity with EF-1alpha makes an extended period of co-occurrence seem unlikely. However, differential loss may play a role in EFL evolution, and this possibility has been less widely discussed. METHODOLOGY/PRINCIPAL FINDINGS: We have undertaken an EST- and PCR-based survey to determine the distribution of these two proteins in a previously under-sampled group, the Euglenozoa. EF-1alpha was found to be widespread and monophyletic, suggesting it is ancestral in this group. EFL was found in some species belonging to each of the three euglenozoan lineages, diplonemids, kinetoplastids, and euglenids. CONCLUSIONS/SIGNIFICANCE: Interestingly, the kinetoplastid EFL sequences are specifically related despite the fact that the lineages in which they are found are not sisters to one another, suggesting that EFL and EF-1alpha co-occurred in an early ancestor of kinetoplastids. This represents the strongest phylogenetic evidence to date that differential loss has contributed to the complex distribution of EFL and EF-1alpha

Molecular Phylogeny and Evolution of Parabasalia with Improved Taxon Sampling and New Protein Markers of Actin and Elongation Factor-1α

BACKGROUND: Inferring the evolutionary history of phylogenetically isolated, deep-branching groups of taxa-in particular determining the root-is often extraordinarily difficult because their close relatives are unavailable as suitable outgroups. One of these taxonomic groups is the phylum Parabasalia, which comprises morphologically diverse species of flagellated protists of ecological, medical, and evolutionary significance. Indeed, previous molecular phylogenetic analyses of members of this phylum have yielded conflicting and possibly erroneous inferences. Furthermore, many species of Parabasalia are symbionts in the gut of termites and cockroaches or parasites and therefore formidably difficult to cultivate, rendering available data insufficient. Increasing the numbers of examined taxa and informative characters (e.g., genes) is likely to produce more reliable inferences. PRINCIPAL FINDINGS: Actin and elongation factor-1α genes were identified newly from 22 species of termite-gut symbionts through careful manipulations and seven cultured species, which covered major lineages of Parabasalia. Their protein sequences were concatenated and analyzed with sequences of previously and newly identified glyceraldehyde-3-phosphate dehydrogenase and the small-subunit rRNA gene. This concatenated dataset provided more robust phylogenetic relationships among major groups of Parabasalia and a more plausible new root position than those previously reported. CONCLUSIONS/SIGNIFICANCE: We conclude that increasing the number of sampled taxa as well as the addition of new sequences greatly improves the accuracy and robustness of the phylogenetic inference. A morphologically simple cell is likely the ancient form in Parabasalia as opposed to a cell with elaborate flagellar and cytoskeletal structures, which was defined as most basal in previous inferences. Nevertheless, the evolution of Parabasalia is complex owing to several independent multiplication and simplification events in these structures. Therefore, systematics based solely on morphology does not reflect the evolutionary history of parabasalids

Algal genomes reveal evolutionary mosaicism and the fate of nucleomorphs

Cryptophyte and chlorarachniophyte algae are transitional forms in the widespread secondary endosymbiotic acquisition of photosynthesis by engulfment of eukaryotic algae. Unlike most secondary plastid-bearing algae, miniaturized versions of the endosymbiont nuclei (nucleomorphs) persist in cryptophytes and chlorarachniophytes. To determine why, and to address other fundamental questions about eukaryote–eukaryote endosymbiosis, we sequenced the nuclear genomes of the cryptophyte Guillardia theta and the chlorarachniophyte Bigelowiella natans. Both genomes have <21, 000 protein genes and are intron rich, and B. natans exhibits unprecedented alternative splicing for a single-celled organism. Phylogenomic analyses and subcellular targeting predictions reveal extensive genetic and biochemical mosaicism, with both host- and endosymbiont-derived genes servicing the mitochondrion, the host cell cytosol, the plastid and the remnant endosymbiont cytosol of both algae. Mitochondrion-to-nucleus gene transfer still occurs in both organisms but plastid-to-nucleus and nucleomorph-to-nucleus transfers do not, which explains why a small residue of essential genes remains locked in each nucleomorph. © 2012 Macmillan Publishers Limited. All rights reserved

Evaluating Variance Estimators for Respondent-Driven Sampling.

Respondent-Driven Sampling (RDS) is a network-based method for sampling hard-to-reach populations that is widely used by public health agencies and researchers worldwide. Estimation of population characteristics from RDS data is challenging due to the unobserved population network, and multiple point and variance estimators have been proposed. Research evaluating these estimators has been limited and largely focused on point estimation; this analysis is the first evaluation of multiple variance estimators currently in use. We evaluated the performance of RDS variance estimators via simulations of RDS on synthetic networked populations constructed from 40 RDS surveys of injection drug users in the United States. In these simulations, average design effects (DEs) were lower and average 95% confidence interval (CI) coverage percentages were higher than suggested in previous work: typical DE range=1-3; average 95% CI coverage=93%. However, DE and CI coverage vary across the 40 sets of simulations, suggesting that the characteristics of a given study should be evaluated to assess estimator performance. We also found that simulation results are sensitive to whether sampling is conducted with replacement and the approach used to create CIs. We conclude that CI coverage rates and DEs are often acceptable but not perfect and that RDS estimates are usually reliable in scenarios where RDS assumptions are met. While RDS estimation performed reasonably well, we found strong evidence that the simple random sample variance estimator and corresponding CIs significantly underestimate variance and should not be used to analyze RDS data

Evaluating Variance Estimators for Respondent-Driven Sampling.

Respondent-Driven Sampling (RDS) is a network-based method for sampling hard-to-reach populations that is widely used by public health agencies and researchers worldwide. Estimation of population characteristics from RDS data is challenging due to the unobserved population network, and multiple point and variance estimators have been proposed. Research evaluating these estimators has been limited and largely focused on point estimation; this analysis is the first evaluation of multiple variance estimators currently in use. We evaluated the performance of RDS variance estimators via simulations of RDS on synthetic networked populations constructed from 40 RDS surveys of injection drug users in the United States. In these simulations, average design effects (DEs) were lower and average 95% confidence interval (CI) coverage percentages were higher than suggested in previous work: typical DE range=1-3; average 95% CI coverage=93%. However, DE and CI coverage vary across the 40 sets of simulations, suggesting that the characteristics of a given study should be evaluated to assess estimator performance. We also found that simulation results are sensitive to whether sampling is conducted with replacement and the approach used to create CIs. We conclude that CI coverage rates and DEs are often acceptable but not perfect and that RDS estimates are usually reliable in scenarios where RDS assumptions are met. While RDS estimation performed reasonably well, we found strong evidence that the simple random sample variance estimator and corresponding CIs significantly underestimate variance and should not be used to analyze RDS data