Between 1 and 1.5 billion years ago, eukaryotic organisms acquired the ability to convert light into chemical energy through endosymbiosis with a Cyanobacterium (e.g.,). This event gave rise to "primary" plastids, which are present in green plants, red algae, and glaucophytes ("Plantae" sensu Cavalier-Smith). The widely accepted view that primary plastids arose only once implies two predictions: (1) all plastids form a monophyletic group, as do (2) primary photosynthetic eukaryotes. Nonetheless, unequivocal support for both predictions is lacking (e.g.,). In this report, we present two phylogenomic analyses, with 50 genes from 16 plastid and 15 cyanobacterial genomes and with 143 nuclear genes from 34 eukaryotic species, respectively. The nuclear dataset includes new sequences from glaucophytes, the less-studied group of primary photosynthetic eukaryotes. We find significant support for both predictions. Taken together, our analyses provide the first strong support for a single endosymbiotic event that gave rise to primary photosynthetic eukaryotes, the Plantae. Because our dataset does not cover the entire eukaryotic diversity (but only four of six major groups in), further testing of the monophyly of Plantae should include representatives from eukaryotic lineages for which currently insufficient sequence information is available.

Genome-scale data sets result in an enhanced resolution of the phylogenetic inference by reducing stochastic errors. However, there is also an increase of systematic errors due to model violations, which can lead to erroneous phylogenies. Here, we explore the impact of systematic errors on the resolution of the eukaryotic phylogeny using a data set of 143 nuclear-encoded proteins from 37 species. The initial observation was that, despite the impressive amount of data, some branches had no significant statistical support. To demonstrate that this lack of resolution is due to a mutual annihilation of phylogenetic and nonphylogenetic signals, we created a series of data sets with slightly different taxon sampling. As expected, these data sets yielded strongly supported but mutually exclusive trees, thus confirming the presence of conflicting phylogenetic and nonphylogenetic signals in the original data set. To decide on the correct tree, we applied several methods expected to reduce the impact of some kinds of systematic error. Briefly, we show that (i) removing fast-evolving positions, (ii) recoding amino acids into functional categories, and (iii) using a site-heterogeneous mixture model (CAT) are three effective means of increasing the ratio of phylogenetic to nonphylogenetic signal. Finally, our results allow us to formulate guidelines for detecting and overcoming phylogenetic artefacts in genome-scale phylogenetic analyses.

We present a new version of miRanalyzer, a web server and stand-alone tool for the detection of known and prediction of new microRNAs in high-throughput sequencing experiments. The new version has been notably improved regarding speed, scope and available features. Alignments are now based on the ultrafast short-read aligner Bowtie (granting also colour space support, allowing mismatches and improving speed) and 31 genomes, including 6 plant genomes, can now be analysed (previous version contained only 7). Differences between plant and animal microRNAs have been taken into account for the prediction models and differential expression of both, known and predicted microRNAs, between two conditions can be calculated. Additionally, consensus sequences of predicted mature and precursor microRNAs can be obtained from multiple samples, which increases the reliability of the predicted microRNAs. Finally, a stand-alone version of the miRanalyzer that is based on a local and easily customized database is also available; this allows the user to have more control on certain parameters as well as to use specific data such as unpublished assemblies or other libraries that are not available in the web server. miRanalyzer is available at http://bioinfo2.ugr.es/miRanalyzer/miRanalyzer.php.

Characterization of biodiversity has been extensively used to confidently monitor and assess environmental status. Yet, visual morphology, traditionally and widely used for species identification in coastal and marine ecosystem communities, is tedious and entails limitations. Metabarcoding coupled with high-throughput sequencing (HTS) represents an alternative to rapidly, accurately, and cost-effectively analyze thousands of environmental samples simultaneously, and this method is increasingly used to characterize the metazoan taxonomic composition of a wide variety of environments. However, a comprehensive study benchmarking visual and metabarcoding-based taxonomic inferences that validates this technique for environmental monitoring is still lacking. Here, we compare taxonomic inferences of benthic macroinvertebrate samples of known taxonomic composition obtained using alternative metabarcoding protocols based on a combination of different DNA sources, barcodes of the mitochondrial cytochrome oxidase I gene and amplification conditions. Our results highlight the influence of the metabarcoding protocol in the obtained taxonomic composition and suggest the better performance of an alternative 313 bp length barcode to the traditionally 658 bp length one used for metazoan metabarcoding. Additionally, we show that a biotic index inferred from the list of macroinvertebrate taxa obtained using DNA-based taxonomic assignments is comparable to that inferred using morphological identification. Thus, our analyses prove metabarcoding valid for environmental status assessment and will contribute to accelerating the implementation of this technique to regular monitoring programs.

According to the chromalveolate hypothesis (Cavalier-Smith T. 1999. Principles of protein and lipid targeting in secondary symbiogenesis: euglenoid, dinoflagellate, and sporozoan plastid origins and the eukaryote family tree. J Eukaryot Microbiol 46:347-366), the four eukaryotic groups with chlorophyll c-containing plastids originate from a single photosynthetic ancestor, which acquired its plastids by secondary endosymbiosis with a red alga. So far, molecular phylogenies have failed to either support or disprove this view. Here, we devise a phylogenomic falsification of the chromalveolate hypothesis that estimates signal strength across the three genomic compartments: If the four chlorophyll c-containing lineages indeed derive from a single photosynthetic ancestor, then similar amounts of plastid, mitochondrial, and nuclear sequences should allow to recover their monophyly. Our results refute this prediction, with statistical support levels too different to be explained by evolutionary rate variation, phylogenetic artifacts, or endosymbiotic gene transfer. Therefore, we reject the chromalveolate hypothesis as falsified in favor of more complex evolutionary scenarios involving multiple higher order eukaryote-eukaryote endosymbioses.

Traditional and emerging human activities are increasingly putting pressures on marine ecosystems and impacting their ability to sustain ecological and human communities. To evaluate the health status of marine ecosystems we need a science-based, integrated Ecosystem Approach, that incorporates knowledge of ecosystem function and services provided that can be used to track how management decisions change the health of marine ecosystems. Although many methods have been developed to assess the status of single components of the ecosystem, few exist for assessing multiple ecosystem components in a holistic way. To undertake such an integrative assessment, it is necessary to understand the response of marine systems to human pressures. Hence, innovative monitoring is needed to obtain data to determine the health of large marine areas, and in an holistic way. Here we review five existing methods that address both of these needs (monitoring and assessment): the Ecosystem Health Assessment Tool; a method for the Marine Strategy Framework Directive in the Bay of Biscay; the Ocean Health Index (OHI); the Marine Biodiversity Assessment Tool, and the Nested Environmental status Assessment Tool. We have highlighted their main characteristics and analyzing their commonalities and differences, in terms of: use of the Ecosystem Approach; inclusion of multiple components in the assessment; use of reference conditions; use of integrative assessments; use of a range of values to capture the status; weighting ecosystem components when integrating; determine the uncertainty; ensure spatial and temporal comparability; use of robust monitoring approaches, and address pressures and impacts. Ultimately, for any ecosystem assessment to be effective it needs to be: transparent and repeatable and, in order to inform marine management, the results should be easy to communicate to wide audiences, including scientists, managers, and policymakers.

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