Regeneration in distantly related species: common strategies and pathways

| openaire: EC/H2020/291002/EU//SIZEFFECTSWhile almost all animals are able to at least partially replace some lost parts, regeneration abilities vary considerably across species. Here we study gene expression patterns in distantly related species to investigate conserved regeneration strategies. To this end, we collect from the literature transcriptomic data obtained during the regeneration of three species (Hydra magnipapillata, Schmidtea mediterranea, and Apostichopus japonicus), and compare them with gene expression during regeneration in vertebrates and mammals. This allows us to identify a common set of differentially expressed genes and relevant shared pathways that are conserved across species during the early stage of the regeneration process. We also find a set of differentially expressed genes that in mammals are associated to the presence of macrophages and to the epithelial–mesenchymal transition. This suggests that features of the sophisticated wound healing strategy of mammals are already observable in earlier emerging metazoans.Peer reviewe

Quantitative analysis of disease-related metabolic dysregulation of human microbiota

Summary: The metabolic activity of all the micro-organism composing the human microbiome interacts with the host metabolism contributing to human health and disease in a way that is not fully understood. Here, we introduce STELLA, a computational method to derive the spectrum of metabolites associated with the microbiome of an individual. STELLA integrates known information on metabolic pathways associated with each bacterial species and extracts from these the list of metabolic products of each singular reaction by means of automatic text analysis. By comparing the result obtained on a single subject with the metabolic profile data of a control set of healthy subjects, we are able to identify individual metabolic alterations. To illustrate the method, we present applications to autism spectrum disorder and multiple sclerosis

Probing spermiogenesis

Classification of morphological features in biological samples is usually performed by a trained eye but the increasing amount of available digital images calls for semi-automatic classification techniques. Here we explore this possibility in the context of acrosome morphological analysis during spermiogenesis. Our method combines feature extraction from three dimensional reconstruction of confocal images with principal component analysis and machine learning. The method could be particularly useful in cases where the amount of data does not allow for a direct inspection by trained eye.Peer reviewe