Chemical defenses in Sacoglossan Opisthobranchs: Taxonomic trends and evolutionary implications

Sacoglossan sea slugs (Mollusca: Opisthobranchia) are one of the few groups of specialist herbivores in the marine environment. Sacoglossans feed suctorially on the cell sap of macroalgae, from which they 'steal' chloroplasts (kleptoplasty) and deterrent substances (kleptochemistry), retaining intracellularly both host plastids and chemicals. The ingested chloroplasts continue to photosynthesize for periods ranging from a few hours or days up to 3 months in some species. Shelled, more primitive sacoglossans feed only on the siphonalean green algal genus Caulerpa, and they do not have functional kleptoplasty. The diet of sacoglossans has radiated out from this ancestral food. Among the shell-less Plakobranchidae (=Elysiidae), the more primitive species feed on other siphonales (families Derbesiaceae, Caulerpaceae, Bryopsidaceae and Codiaceae) and fix carbon, while the more 'advanced' species within the Plakobranchidae and Limapontioidae have a more broad dietary range. Most of these 'advanced' species are unable to fix carbon because the chloroplasts of their food algae are mechanically disrupted during ingestion. Mesoherbivores are likely to be eaten if they live on palatable seaweeds, their cryptic coloration and form not always keeping them safe from predators. Sacoglossans prefer to live on and eat chemically defended seaweeds, and they use ingested algal chemicals as deterrents of potential predators. The most ancestral shelled sacoglossans (Oxynoidae) and some Plakobranchidae such as Elysia translucens, Thuridilla hopei and Bosellia mimetica have developed a diet-derived chemical defense mechanism. Oxynoids and Thuridilla hopei are able to biomodify the algal metabolites. However, the Plakobranchidae Elysia timida and E. viridis, together with Limapontioidea species, are characterized by their ability to de novo synthesize polypropionate metabolites. A whole analysis of kleptoplasty and chemical defenses in sacoglossans may offer a better understanding of the ecology and evolution of these specialized opisthobranchs. In this paper we summarize some of the latest findings, related mainly to Mediterranean species, and offer a plausible evolutionary scenario based on the biological and chemical trends we can distinguish in them.

Delphinium L. subgen Delphinium: origin and evolutionary trends

Les caractéristiques avançades de Delphinium L. subgèn. Delphinium (taxons anuals) son comparades amb les del subgèn. Delphinastrum (DC.) Wang i del subgèn. Oligophyllon Dimitrova (taxons perennes). La morfología floral mostra un intercanvi de funcions entre els petals laterals i els petals superiors i restructura de la inflorescencia de molts taxons anuals afavoreix un augment de les taxes de geitonogàmia-autogàmia. L'evolució dels cariotips és basada en una disminució de la longitud total dels cromosomes i en un increment del grau d'asimetria; el nombre cromosómic roman constant per a totes les especies anuals (2n = 16). Leficàcia de la dispersió de les especies anuals és mes gran que no pas la de les especies perennes, per causa d'un increment en la producció de granes i per l'augment de la flotabilitat, tant a l'aire com a l'aigua. D'altres caractéristiques adaptatives avançades son l'adquisició de noves defenses químiques i l'aparició d'un nou tipus embriogènic. Els nínxols ecologies del subgèn. Delphinium corresponen a habitats oberts i alterats, en comparado amb els habitats estables i relativament tancats dels subgéneros Delphinastrum i Oligophyllon. Es presenta una hipótesi global de les tendencies évolutives observades en anuals vs. perennes en connexió amb consideracions biogeogràfiques, així corn un resum taxonomic final.

A system-level, molecular evolutionary analysis of mammalian phototransduction

Visual perception is initiated in the photoreceptor cells of the retina via the phototransduction system.This system has shown marked evolution during mammalian divergence in such complex attributes as activation time and recovery time. We have performed a molecular evolutionary analysis of proteins involved in mammalianphototransduction in order to unravel how the action of natural selection has been distributed throughout thesystem to evolve such traits. We found selective pressures to be non-randomly distributed according to both a simple protein classification scheme and a protein-interaction network representation of the signaling pathway. Proteins which are topologically central in the signaling pathway, such as the G proteins, as well as retinoid cycle chaperones and proteins involved in photoreceptor cell-type determination, were found to be more constrained in their evolution. Proteins peripheral to the pathway, such as ion channels and exchangers, as well as the retinoid cycle enzymes, have experienced a relaxation of selective pressures. Furthermore, signals of positive selection were detected in two genes: the short-wave (blue) opsin (OPN1SW) in hominids and the rod-specific Na+/Ca2+,K+ ion exchanger (SLC24A1) in rodents. The functions of the proteins involved in phototransduction and the topology of the interactions between them have imposed non-random constraints on their evolution. Thus, in shaping or conserving system-level phototransduction traits, natural selection has targeted the underlying proteins in a concerted manner.

Differences in the evolutionary history of disease genes affected by dominant or recessive mutations

Background: Global analyses of human disease genes by computational methods have yielded important advances in the understanding of human diseases. Generally these studies have treated the group of disease genes uniformly, thus ignoring the type of disease-causing mutations (dominant or recessive). In this report we present a comprehensive study of the evolutionary history of autosomal disease genes separated by mode of inheritance.Results: We examine differences in protein and coding sequence conservation between dominant and recessive human disease genes. Our analysis shows that disease genes affected by dominant mutations are more conserved than those affected by recessive mutations. This could be a consequence of the fact that recessive mutations remain hidden from selection while heterozygous. Furthermore, we employ functional annotation analysis and investigations into disease severity to support this hypothesis. Conclusion: This study elucidates important differences between dominantly- and recessively-acting disease genes in terms of protein and DNA sequence conservation, paralogy and essentiality. We propose that the division of disease genes by mode of inheritance will enhance both understanding of the disease process and prediction of candidate disease genes in the future.

Gorilla genome structural variation reveals evolutionary parallelisms with chimpanzee

Structural variation has played an important role in the evolutionary restructuring of human and great ape genomes. Recent analyses have suggested that the genomes of chimpanzee and human have been particularly enriched for this form of genetic variation. Here, we set out to assess the extent of structural variation in the gorilla lineage by generating 10-fold genomic sequence coverage from a western lowland gorilla and integrating these data into a physical and cytogenetic framework of structural variation. We discovered and validated over 7665 structural changes within the gorilla lineage, including sequence resolution of inversions, deletions, duplications, and mobile element insertions. A comparison with human and other ape genomes shows that the gorilla genome has been subjected to the highest rate of segmental duplication. We show that both the gorilla and chimpanzee genomes have experienced independent yet convergent patterns of structural mutation that have not occurred in humans, including the formation of subtelomeric heterochromatic caps, the hyperexpansion of segmental duplications, and bursts of retroviral integrations. Our analysis suggests that the chimpanzee and gorilla genomes are structurally more derived than either orangutan or human genomes.

Evolutionary analysis of genetic variants involved in rare diseases

Next-generation sequencing techniques such as exome sequencing can successfully detect all genetic variants in a human exome and it has been useful together with the implementation of variant filters to identify causing-disease mutations. Two filters aremainly used for the mutations identification: low allele frequency and the computational annotation of the genetic variant. Bioinformatic tools to predict the effect of a givenvariant may have errors due to the existing bias in databases and sometimes show a limited coincidence among them. Advances in functional and comparative genomics are needed in order to properly annotate these variants.The goal of this study is to: first, functionally annotate Common Variable Immunodeficiency disease (CVID) variants with the available bioinformatic methods in order to assess the reliability of these strategies. Sencondly, as the development of new methods to reduce the number of candidate genetic variants is an active and necessary field of research, we are exploring the utility of gene function information at organism level as a filter for rare disease genes identification. Recently, it has been proposed that only 10-15% of human genes are essential and therefore we would expect that severe rare diseases are mostly caused by mutations on them. Our goal is to determine whether or not these rare and severe diseases are caused by deleterious mutations in these essential genes. If this hypothesis were true, taking into account essential genes as a filter would be an interesting parameter to identify causingdisease mutations.

Fast Approximation of Nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems

This paper proposes a very fast method for blindly approximating a nonlinear mapping which transforms a sum of random variables. The estimation is surprisingly good even when the basic assumption is not satisfied.We use the method for providing a good initialization for inverting post-nonlinear mixtures and Wiener systems. Experiments show that the algorithm speed is strongly improved and the asymptotic performance is preserved with a very low extra computational cost.

Coherency and sharpness measures by using ICA algorithms. An investigation for Alzheimer’s disease discrimination

In this paper, we present a comprehensive study of different Independent Component Analysis (ICA) algorithms for the calculation of coherency and sharpness of electroencephalogram (EEG) signals, in order to investigate the possibility of early detection of Alzheimer’s disease (AD). We found that ICA algorithms can help in the artifact rejection and noise reduction, improving the discriminative property of features in high frequency bands (specially in high alpha and beta ranges). In addition to different ICA algorithms, the optimum number of selected components is investigated, in order to help decision processes for future works.

Investigation of ICA algorithms for feature extraction of EEG signals in discrimination of Alzheimer disease

In this paper we present a quantitative comparisons of different independent component analysis (ICA) algorithms in order to investigate their potential use in preprocessing (such as noise reduction and feature extraction) the electroencephalogram (EEG) data for early detection of Alzhemier disease (AD) or discrimination between AD (or mild cognitive impairment, MCI) and age-match control subjects.

Comparing Random-based and k-Anonymity-Based Algorithms for Graph Anonymization

Recently, several anonymization algorithms have appeared for privacy preservation on graphs. Some of them are based on random-ization techniques and on k-anonymity concepts. We can use both of them to obtain an anonymized graph with a given k-anonymity value. In this paper we compare algorithms based on both techniques in orderto obtain an anonymized graph with a desired k-anonymity value. We want to analyze the complexity of these methods to generate anonymized graphs and the quality of the resulting graphs.

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen

Comment on 'The Molecular Evolutionary Patterns of the Insulin/FOXO Signaling Pathway'

Letter to the Editor on Wang M, Wang Q, Wang Z, Zhang X, Pan Y. The molecular evolutionary patterns of the insulin/FOXO signaling pathway

Genome-wide DNA polymorphism analyses using VariScan

BACKGROUND: DNA sequence polymorphisms analysis can provide valuable information on the evolutionary forces shaping nucleotide variation, and provides an insight into the functional significance of genomic regions. The recent ongoing genome projects will radically improve our capabilities to detect specific genomic regions shaped by natural selection. Current available methods and software, however, are unsatisfactory for such genome-wide analysis. RESULTS: We have developed methods for the analysis of DNA sequence polymorphisms at the genome-wide scale. These methods, which have been tested on a coalescent-simulated and actual data files from mouse and human, have been implemented in the VariScan software package version 2.0. Additionally, we have also incorporated a graphical-user interface. The main features of this software are: i) exhaustive population-genetic analyses including those based on the coalescent theory; ii) analysis adapted to the shallow data generated by the high-throughput genome projects; iii) use of genome annotations to conduct a comprehensive analyses separately for different functional regions; iv) identification of relevant genomic regions by the sliding-window and wavelet-multiresolution approaches; v) visualization of the results integrated with current genome annotations in commonly available genome browsers. CONCLUSION: VariScan is a powerful and flexible suite of software for the analysis of DNA polymorphisms. The current version implements new algorithms, methods, and capabilities, providing an important tool for an exhaustive exploratory analysis of genome-wide DNA polymorphism data.

Computational and Energy Costs of Cryptographic Algorithms on Handheld Devices

Networks are evolving toward a ubiquitous model in which heterogeneousdevices are interconnected. Cryptographic algorithms are required for developing securitysolutions that protect network activity. However, the computational and energy limitationsof network devices jeopardize the actual implementation of such mechanisms. In thispaper, we perform a wide analysis on the expenses of launching symmetric and asymmetriccryptographic algorithms, hash chain functions, elliptic curves cryptography and pairingbased cryptography on personal agendas, and compare them with the costs of basic operatingsystem functions. Results show that although cryptographic power costs are high and suchoperations shall be restricted in time, they are not the main limiting factor of the autonomyof a device.

Robust power allocation algorithms for MIMO OFDM systems with imperfect CSI

This paper presents a Bayesian approach to the design of transmit prefiltering matrices in closed-loop schemes robust to channel estimation errors. The algorithms are derived for a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. Two different optimizationcriteria are analyzed: the minimization of the mean square error and the minimization of the bit error rate. In both cases, the transmitter design is based on the singular value decomposition (SVD) of the conditional mean of the channel response, given the channel estimate. The performance of the proposed algorithms is analyzed,and their relationship with existing algorithms is indicated. As withother previously proposed solutions, the minimum bit error rate algorithmconverges to the open-loop transmission scheme for very poor CSI estimates.