Evidence for Centromere Drive in the Holocentric Chromosomes of Caenorhabditis

In monocentric organisms with asymmetric meiosis, the kinetochore proteins, such as CENH3 and CENP-C, evolve adaptively to counterbalance the deleterious effects of centromere drive, which is caused by the expansion of centromeric satellite repeats. The selection regimes that act on CENH3 and CENP-C genes have not been analyzed in organisms with holocentric chromosomes, although holocentrism is speculated to have evolved to suppress centromere drive. We tested both CENH3 and CENP-C for positive selection in several species of the holocentric genus Caenorhabditis using the maximum likelihood approach and sliding-window analysis. Although CENP-C did not show any signs of positive selection, positive selection has been detected in the case of CENH3. These results support the hypothesis that centromere drive occurs in Nematoda, at least in the telokinetic meiosis of Caenorhabditis

Gene Promoter Evolution Targets the Center of the Human Protein Interaction Network

Assessing the contribution of promoters and coding sequences to gene evolution is an important step toward discovering the major genetic determinants of human evolution. Many specific examples have revealed the evolutionary importance of cis-regulatory regions. However, the relative contribution of regulatory and coding regions to the evolutionary process and whether systemic factors differentially influence their evolution remains unclear. To address these questions, we carried out an analysis at the genome scale to identify signatures of positive selection in human proximal promoters. Next, we examined whether genes with positively selected promoters (Prom+ genes) show systemic differences with respect to a set of genes with positively selected protein-coding regions (Cod+ genes). We found that the number of genes in each set was not significantly different (8.1% and 8.5%, respectively). Furthermore, a functional analysis showed that, in both cases, positive selection affects almost all biological processes and only a few genes of each group are located in enriched categories, indicating that promoters and coding regions are not evolutionarily specialized with respect to gene function. On the other hand, we show that the topology of the human protein network has a different influence on the molecular evolution of proximal promoters and coding regions. Notably, Prom+ genes have an unexpectedly high centrality when compared with a reference distribution (P = 0.008, for Eigenvalue centrality). Moreover, the frequency of Prom+ genes increases from the periphery to the center of the protein network (P = 0.02, for the logistic regression coefficient). This means that gene centrality does not constrain the evolution of proximal promoters, unlike the case with coding regions, and further indicates that the evolution of proximal promoters is more efficient in the center of the protein network than in the periphery. These results show that proximal promoters have had a systemic contribution to human evolution by increasing the participation of central genes in the evolutionary process

Accounting For Alignment Uncertainty in Phylogenomics

Uncertainty in multiple sequence alignments has a large impact on phylogenetic analyses. Little has been done to evaluate the quality of individual positions in protein sequence alignments, which directly impact the accuracy of phylogenetic trees. Here we describe ZORRO, a probabilistic masking program that accounts for alignment uncertainty by assigning confidence scores to each alignment position. Using the BALIBASE database and in simulation studies, we demonstrate that masking by ZORRO significantly reduces the alignment uncertainty and improves the tree accuracy

PICS-Ord: unlimited coding of ambiguous regions by pairwise identity and cost scores ordination

<p>Abstract</p> <p>Background</p> <p>We present a novel method to encode ambiguously aligned regions in fixed multiple sequence alignments by 'Pairwise Identity and Cost Scores Ordination' (PICS-Ord). The method works via ordination of sequence identity or cost scores matrices by means of Principal Coordinates Analysis (PCoA). After identification of ambiguous regions, the method computes pairwise distances as sequence identities or cost scores, ordinates the resulting distance matrix by means of PCoA, and encodes the principal coordinates as ordered integers. Three biological and 100 simulated datasets were used to assess the performance of the new method.</p> <p>Results</p> <p>Including ambiguous regions coded by means of PICS-Ord increased topological accuracy, resolution, and bootstrap support in real biological and simulated datasets compared to the alternative of excluding such regions from the analysis a priori. In terms of accuracy, PICS-Ord performs equal to or better than previously available methods of ambiguous region coding (e.g., INAASE), with the advantage of a practically unlimited alignment size and increased analytical speed and the possibility of PICS-Ord scores to be analyzed together with DNA data in a partitioned maximum likelihood model.</p> <p>Conclusions</p> <p>Advantages of PICS-Ord over step matrix-based ambiguous region coding with INAASE include a practically unlimited number of OTUs and seamless integration of PICS-Ord codes into phylogenetic datasets, as well as the increased speed of phylogenetic analysis. Contrary to word- and frequency-based methods, PICS-Ord maintains the advantage of pairwise sequence alignment to derive distances, and the method is flexible with respect to the calculation of distance scores. In addition to distance and maximum parsimony, PICS-Ord codes can be analyzed in a Bayesian or maximum likelihood framework. RAxML (version 7.2.6 or higher that was developed for this study) allows up to 32-state ordered or unordered characters. A GTR, MK, or ORDERED model can be applied to analyse the PICS-Ord codes partition, with GTR performing slightly better than MK and ORDERED.</p> <p>Availability</p> <p>An implementation of the PICS-Ord algorithm is available from <url>http://scit.us/projects/ngila/wiki/PICS-Ord</url>. It requires both the statistical software, R <url>http://www.r-project.org</url> and the alignment software Ngila <url>http://scit.us/projects/ngila</url>.</p

Modeling the Evolution of Regulatory Elements by Simultaneous Detection and Alignment with Phylogenetic Pair HMMs

The computational detection of regulatory elements in DNA is a difficult but important problem impacting our progress in understanding the complex nature of eukaryotic gene regulation. Attempts to utilize cross-species conservation for this task have been hampered both by evolutionary changes of functional sites and poor performance of general-purpose alignment programs when applied to non-coding sequence. We describe a new and flexible framework for modeling binding site evolution in multiple related genomes, based on phylogenetic pair hidden Markov models which explicitly model the gain and loss of binding sites along a phylogeny. We demonstrate the value of this framework for both the alignment of regulatory regions and the inference of precise binding-site locations within those regions. As the underlying formalism is a stochastic, generative model, it can also be used to simulate the evolution of regulatory elements. Our implementation is scalable in terms of numbers of species and sequence lengths and can produce alignments and binding-site predictions with accuracy rivaling or exceeding current systems that specialize in only alignment or only binding-site prediction. We demonstrate the validity and power of various model components on extensive simulations of realistic sequence data and apply a specific model to study Drosophila enhancers in as many as ten related genomes and in the presence of gain and loss of binding sites. Different models and modeling assumptions can be easily specified, thus providing an invaluable tool for the exploration of biological hypotheses that can drive improvements in our understanding of the mechanisms and evolution of gene regulation

Alignment-Free Phylogenetic Reconstruction

14th Annual International Conference, RECOMB 2010, Lisbon, Portugal, April 25-28, 2010. ProceedingsWe introduce the first polynomial-time phylogenetic reconstruction algorithm under a model of sequence evolution allowing insertions and deletions (or indels). Given appropriate assumptions, our algorithm requires sequence lengths growing polynomially in the number of leaf taxa. Our techniques are distance-based and largely bypass the problem of multiple alignment

eggNOG v2.0: extending the evolutionary genealogy of genes with enhanced non-supervised orthologous groups, species and functional annotations

The identification of orthologous relationships forms the basis for most comparative genomics studies. Here, we present the second version of the eggNOG database, which contains orthologous groups (OGs) constructed through identification of reciprocal best BLAST matches and triangular linkage clustering. We applied this procedure to 630 complete genomes (529 bacteria, 46 archaea and 55 eukaryotes), which is a 2-fold increase relative to the previous version. The pipeline yielded 224 847 OGs, including 9724 extended versions of the original COG and KOG. We computed OGs for different levels of the tree of life; in addition to the species groups included in our first release (i.e. fungi, metazoa, insects, vertebrates and mammals), we have now constructed OGs for archaea, fishes, rodents and primates. We automatically annotate the non-supervised orthologous groups (NOGs) with functional descriptions, protein domains, and functional categories as defined initially for the COG/KOG database. In-depth analysis is facilitated by precomputed high-quality multiple sequence alignments and maximum-likelihood trees for each of the available OGs. Altogether, eggNOG covers 2 242 035 proteins (built from 2 590 259 proteins) and provides a broad functional description for at least 1 966 709 (88%) of them. Users can access the complete set of orthologous groups via a web interface at: http://eggnog.embl.de

Genomic analysis of Sparus aurata reveals the evolutionary dynamics of sex-biased genes in a sequential hermaphrodite fish

Sexual dimorphism is a fascinating subject in evolutionary biology and mostly results from sex-biased expression of genes, which have been shown to evolve faster in gonochoristic species. We report here genome and sex-specific transcriptome sequencing of Sparus aurata, a sequential hermaphrodite fish. Evolutionary comparative analysis reveals that sex-biased genes in S. aurata are similar in number and function, but evolved following strikingly divergent patterns compared with gonochoristic species, showing overall slower rates because of stronger functional constraints. Fast evolution is observed only for highly ovary-biased genes due to female-specific patterns of selection that are related to the peculiar reproduction mode of S. aurata, first maturing as male, then as female. To our knowledge, these findings represent the first genome-wide analysis on sex-biased loci in a hermaphrodite vertebrate species, demonstrating how having two sexes in the same individual profoundly affects the fate of a large set of evolutionarily relevant genes.European Union KBBE.2013.1.2-10 European Community 311920 Fondazione Cassa di Risparmio Padova e Rovigo FCT - Foundation for Science and Technology research grant SPARCOMP under the Call ARISTEIA I of the National Strategic Reference Framework - by the EU 36 Hellenic Republic through the European Social Fundinfo:eu-repo/semantics/publishedVersio

Spinning Gland Transcriptomics from Two Main Clades of Spiders (Order: Araneae) - Insights on Their Molecular, Anatomical and Behavioral Evolution

Characterized by distinctive evolutionary adaptations, spiders provide a comprehensive system for evolutionary and developmental studies of anatomical organs, including silk and venom production. Here we performed cDNA sequencing using massively parallel sequencers (454 GS-FLX Titanium) to generate ∼80,000 reads from the spinning gland of Actinopus spp. (infraorder: Mygalomorphae) and Gasteracantha cancriformis (infraorder: Araneomorphae, Orbiculariae clade). Actinopus spp. retains primitive characteristics on web usage and presents a single undifferentiated spinning gland while the orbiculariae spiders have seven differentiated spinning glands and complex patterns of web usage. MIRA, Celera Assembler and CAP3 software were used to cluster NGS reads for each spider. CAP3 unigenes passed through a pipeline for automatic annotation, classification by biological function, and comparative transcriptomics. Genes related to spider silks were manually curated and analyzed. Although a single spidroin gene family was found in Actinopus spp., a vast repertoire of specialized spider silk proteins was encountered in orbiculariae. Astacin-like metalloproteases (meprin subfamily) were shown to be some of the most sampled unigenes and duplicated gene families in G. cancriformis since its evolutionary split from mygalomorphs. Our results confirm that the evolution of the molecular repertoire of silk proteins was accompanied by the (i) anatomical differentiation of spinning glands and (ii) behavioral complexification in the web usage. Finally, a phylogenetic tree was constructed to cluster most of the known spidroins in gene clades. This is the first large-scale, multi-organism transcriptome for spider spinning glands and a first step into a broad understanding of spider web systems biology and evolution