April 2013 archive

Related Articles

Nucleomorph genome sequence of the cryptophyte alga Chroomonas mesostigmatica CCMP1168 reveals lineage-specific gene loss and genome complexity.

Genome Biol Evol. 2012;4(11):1162-75

Authors: Moore CE, Curtis B, Mills T, Tanifuji G, Archibald JM

Abstract
Cryptophytes are a diverse lineage of marine and freshwater, photosynthetic and secondarily nonphotosynthetic algae that acquired their plastids (chloroplasts) by “secondary” (i.e., eukaryote-eukaryote) endosymbiosis. Consequently, they are among the most genetically complex cells known and have four genomes: a mitochondrial, plastid, “master” nuclear, and residual nuclear genome of secondary endosymbiotic origin, the so-called “nucleomorph” genome. Sequenced nucleomorph genomes are ∼1,000-kilobase pairs (Kbp) or less in size and are comprised of three linear, compositionally biased chromosomes. Although most functionally annotated nucleomorph genes encode proteins involved in core eukaryotic processes, up to 40% of the genes in these genomes remain unidentifiable. To gain insight into the function and evolutionary fate of nucleomorph genomes, we used 454 and Illumina technologies to completely sequence the nucleomorph genome of the cryptophyte Chroomonas mesostigmatica CCMP1168. At 702.9 Kbp in size, the C. mesostigmatica nucleomorph genome is the largest and the most complex nucleomorph genome sequenced to date. Our comparative analyses reveal the existence of a highly conserved core set of genes required for maintenance of the cryptophyte nucleomorph and plastid, as well as examples of lineage-specific gene loss resulting in differential loss of typical eukaryotic functions, e.g., proteasome-mediated protein degradation, in the four cryptophyte lineages examined.

PMID: 23042551 [PubMed – indexed for MEDLINE]

Related Articles

Measuring community similarity with phylogenetic networks.

Mol Biol Evol. 2012 Dec;29(12):3947-58

Authors: Parks DH, Beiko RG

Abstract
Environmental drivers of biodiversity can be identified by relating patterns of community similarity to ecological factors. Community variation has traditionally been assessed by considering changes in species composition and more recently by incorporating phylogenetic information to account for the relative similarity of taxa. Here, we describe how an important class of measures including Bray-Curtis, Canberra, and UniFrac can be extended to allow community variation to be computed on a phylogenetic network. We focus on phylogenetic split systems, networks that are produced by the widely used median network and neighbor-net methods, which can represent incongruence in the evolutionary history of a set of taxa. Calculating β diversity over a split system provides a measure of community similarity averaged over uncertainty or conflict in the available phylogenetic signal. Our freely available software, Network Diversity, provides 11 qualitative (presence-absence, unweighted) and 14 quantitative (weighted) network-based measures of community similarity that model different aspects of community richness and evenness. We demonstrate the broad applicability of network-based diversity approaches by applying them to three distinct data sets: pneumococcal isolates from distinct geographic regions, human mitochondrial DNA data from the Indonesian island of Nias, and proteorhodopsin sequences from the Sargasso and Mediterranean Seas. Our results show that major expected patterns of variation for these data sets are recovered using network-based measures, which indicates that these patterns are robust to phylogenetic uncertainty and conflict. Nonetheless, network-based measures of community similarity can differ substantially from measures ignoring phylogenetic relationships or from tree-based measures when incongruent signals are present in the underlying data. Network-based measures provide a methodology for assessing the robustness of β-diversity results in light of incongruent phylogenetic signal and allow β diversity to be calculated over widely used network structures such as median networks.

PMID: 22915830 [PubMed – indexed for MEDLINE]

Related Articles
Problems with estimation of ancestral frequencies under stationary models.
Syst Biol. 2013 Mar;62(2):330-8
Authors: Susko E, Roger AJ
PMID: 22949482 [PubMed – indexed for MEDLINE]