Myxosporea (Myxozoa, Cnidaria) Lack DNA Cytosine Methylation.

DNA cytosine methylation is central to many biological processes, including regulation of gene expression, cellular differentiation, and development. This DNA modification is conserved across animals, having been found in representatives of sponges, ctenophores, cnidarians, and bilaterians, and with very few known instances of secondary loss in animals. Myxozoans are a group of microscopic, obligate endoparasitic cnidarians that have lost many genes over the course of their evolution from free-living ancestors. Here, we investigated the evolution of the key enzymes involved in DNA cytosine methylation in 29 cnidarians and found that these enzymes were lost in an ancestor of Myxosporea (the most speciose class of Myxozoa). Additionally, using whole-genome bisulfite sequencing, we confirmed that the genomes of two distant species of myxosporeans, Ceratonova shasta and Henneguya salminicola, completely lack DNA cytosine methylation. Our results add a notable and novel taxonomic group, the Myxosporea, to the very short list of animal taxa lacking DNA cytosine methylation, further illuminating the complex evolutionary history of this epigenetic regulatory mechanism.

In silico approach to predict pancreatic ß-cells classically secreted proteins.

Pancreatic ß-cells, residents of the islets of Langerhans, are the unique insulin-producers in the body. Their physiology is a topic of intensive studies aiming to understand the biology of insulin production and its role in diabetes pathology. However, investigations about these cells' subset of secreted proteins, the secretome, are surprisingly scarce and a list describing islet/ß-cell secretome upon glucose-stimulation is not yet available. In silico predictions of secretomes are an interesting approach that can be employed to forecast proteins likely to be secreted. In this context, using the rationale behind classical secretion of proteins through the secretory pathway, a Python tool capable of predicting classically secreted proteins was developed. This tool was applied to different available proteomic data (human and rodent islets, isolated ß-cells, ß-cell secretory granules, and ß-cells supernatant), filtering them in order to selectively list only classically secreted proteins. The method presented here can retrieve, organize, search and filter proteomic lists using UniProtKB as a central database. It provides analysis by overlaying different sets of information, filtering out potential contaminants and clustering the identified proteins into functional groups. A range of 70-92% of the original proteomes analyzed was reduced generating predicted secretomes. Islet and ß-cell signal peptide-containing proteins, and endoplasmic reticulum-resident proteins were identified and quantified. From the predicted secretomes, exemplary conservational patterns were inferred, as well as the signaling pathways enriched within them. Such a technique proves to be an effective approach to reduce the horizon of plausible targets for drug development or biomarkers identification.