The freshwater cnidarian Hydra was first described in 17021 and has been the object of study for 300 years. Experimental studies of Hydra between 1736 and 1744 culminated in the discovery of asexual reproduction of an animal by budding, the first description of regeneration in an animal, and successful transplantation of tissue between animals2. Today, Hydra is an important model for studies of axial patterning3, stem cell biology4 and regeneration5. Here we report the genome of Hydra magnipapillata and compare it to the genomes of the anthozoan Nematostella vectensis6 and other animals. The Hydra genome has been shaped by bursts of transposable element expansion, horizontal gene transfer, trans-splicing, and simplification of gene structure and gene content that parallel simplification of the Hydra life cycle. We also report the sequence of the genome of a novel bacterium stably associated with H. magnipapillata. Comparisons of the Hydra genome to the genomes of other animals shed light on the evolution of epithelia, contractile tissues, developmentally regulated transcription factors, the Spemann–Mangold organizer, pluripotency genes and the neuromuscular junction.

BackgroundIt was long assumed that proteins are at least 100 amino acids (AAs) long. Moreover, the detection of short translation products (e.g. coded from small Open Reading Frames, sORFs) is very difficult as the short length makes it hard to distinguish true coding ORFs from ORFs occurring by chance. Nevertheless, over the past few years many such non-canonical genes (with ORFs < 100 AAs) have been discovered in different organisms like Arabidopsis thaliana, Saccharomyces cerevisiae, and Drosophila melanogaster. Thanks to advances in sequencing, bioinformatics and computing power, it is now possible to scan the genome in unprecedented scrutiny, for example in a search of this type of small ORFs.ResultsUsing bioinformatics methods, we performed a systematic search for putatively functional sORFs in the Mus musculus genome. A genome-wide scan detected all sORFs which were subsequently analyzed for their coding potential, based on evolutionary conservation at the AA level, and ranked using a Support Vector Machine (SVM) learning model. The ranked sORFs are finally overlapped with ribosome profiling data, hinting to sORF translation. All candidates are visually inspected using an in-house developed genome browser. In this way dozens of highly conserved sORFs, targeted by ribosomes were identified in the mouse genome, putatively encoding micropeptides.ConclusionOur combined genome-wide approach leads to the prediction of a comprehensive but manageable set of putatively coding sORFs, a very important first step towards the identification of a new class of bioactive peptides, called micropeptides.

The evolutionary origins of neurons remain unknown. Although recent genome data of extant early-branching animals have shown that neural genes existed in the common ancestor of animals, the physiological and genetic properties of neurons in the early evolutionary phase are still unclear. Here, we performed a mass spectrometry-based comprehensive survey of short peptides from early-branching lineages Cnidaria, Porifera and Ctenophora. We identified a number of mature ctenophore neuropeptides that are expressed in neurons associated with sensory, muscular and digestive systems. The ctenophore peptides are stored in vesicles in cell bodies and neurites, suggesting volume transmission similar to that of cnidarian and bilaterian peptidergic systems. A comparison of genetic characteristics revealed that the peptide-expressing cells of Cnidaria and Ctenophora express the vast majority of genes that have pivotal roles in maturation, secretion and degradation of neuropeptides in Bilateria. Functional analysis of neuropeptides and prediction of receptors with machine learning demonstrated peptide regulation of a wide range of target effector cells, including cells of muscular systems. The striking parallels between the peptidergic neuronal properties of Cnidaria and Bilateria and those of Ctenophora, the most basal neuron-bearing animals, suggest a common evolutionary origin of metazoan peptidergic nervous systems.

When studying the set of biologically active peptides (the so-called peptidome) of a cell type, organ, or entire organism, the identification of peptides is mostly attempted by MS. However, identification rates are often dismally unsatisfactory. A great deal of failed or missed identifications may be attributable to the wealth of modifications on peptides, some of which may originate from in vivo post-translational processes to activate the molecule, whereas others could be introduced during the tissue preparation procedures. Preliminary knowledge of the modification profile of specific peptidome samples would greatly improve identification rates. To this end we developed an approach that performs clustering of mass spectra in a way that allows us to group spectra having similar peak patterns over significant segments. Comparing members of one spectral group enables us to assess the modifications (expressed as mass shifts in Dalton) present in a peptidome sample. The clustering algorithm in this study is called Bonanza, and it was applied to MALDI-TOF/TOF MS spectra from the mouse. Peptide identification rates went up from 17 to 36% for 278 spectra obtained from the pancreatic islets and from 21 to 43% for 163 pituitary spectra. Spectral clustering with subsequent advanced database search may result in the discovery of new biologically active peptides and modifications thereof, as shown by this report indeed.

Neuropeptides are a class of bioactive peptides shown to be involved in various physiological processes, including metabolism, development, and reproduction. Although neuropeptide candidates have been predicted from genomic and transcriptomic data, comprehensive characterization of neuropeptide repertoires remains a challenge owing to their small size and variable sequences. De novo prediction of neuropeptides from genome or transcriptome data is difficult and usually only efficient for those peptides that have identified orthologs in other animal species. Recent peptidomics technology has enabled systematic structural identification of neuropeptides by using the combination of liquid chromatography and tandem mass spectrometry. However, reliable identification of naturally occurring peptides using a conventional tandem mass spectrometry approach, scanning spectra against a protein database, remains difficult because a large search space must be scanned due to the absence of a cleavage enzyme specification. We developed a pipeline consisting of in silico prediction of candidate neuropeptides followed by peptide-spectrum matching. This approach enables highly sensitive and reliable neuropeptide identification, as the search space for peptide-spectrum matching is highly reduced. Nematostella vectensis is a basal eumetazoan with one of the most ancient nervous systems. We scanned the Nematostella protein database for sequences displaying structural hallmarks typical of eumetazoan neuropeptide precursors, including amino- and carboxyterminal motifs and associated modifications. Peptide-spectrum matching was performed against a dataset of peptides that are cleaved in silico from these putative peptide precursors. The dozens of newly identified neuropeptides display structural similarities to bilaterian neuropeptides including tachykinin, myoinhibitory peptide, and neuromedin-U/pyrokinin, suggesting these neuropeptides occurred in the eumetazoan ancestor of all animal species.