Internal lipid synthesis and vesicle growth as a step toward self-reproduction of the minimal cell

One of the major properties of the semi-synthetic minimal cell, as a model for early living cells, is the ability to self-reproduce itself, and the reproduction of the boundary layer or vesicle compartment is part of this process. A minimal bio-molecular mechanism based on the activity of one single enzyme, the FAS-B (Fatty Acid Synthase) Type I enzyme from Brevibacterium ammoniagenes, is encapsulated in 1-palmitoyl-2oleoyl-sn-glycero-3-phosphatidylcholine (POPC) liposomes to control lipid synthesis. Consequently molecules of palmitic acid released from the FAS catalysis, within the internal lumen, move toward the membrane compartment and become incorporated into the phospholipid bilayer. As a result the vesicle membranes change in lipid composition and liposome growth can be monitored. Here we report the first experiments showing vesicles growth by catalysis of one enzyme only that produces cell boundary from within. This is the prototype of the simplest autopoietic minimal cell

Do Natural Proteins Differ from Random Sequences Polypeptides? Natural vs. Random Proteins Classification Using an Evolutionary Neural Network

Are extant proteins the exquisite result of natural selection or are they random sequences slightly edited by evolution? This question has puzzled biochemists for long time and several groups have addressed this issue comparing natural protein sequences to completely random ones coming to contradicting conclusions. Previous works in literature focused on the analysis of primary structure in an attempt to identify possible signature of evolutionary editing. Conversely, in this work we compare a set of 762 natural proteins with an average length of 70 amino acids and an equal number of completely random ones of comparable length on the basis of their structural features. We use an ad hoc Evolutionary Neural Network Algorithm (ENNA) in order to assess whether and to what extent natural proteins are edited from random polypeptides employing 11 different structure-related variables (i.e. net charge, volume, surface area, coil, alpha helix, beta sheet, percentage of coil, percentage of alpha helix, percentage of beta sheet, percentage of secondary structure and surface hydrophobicity). The ENNA algorithm is capable to correctly distinguish natural proteins from random ones with an accuracy of 94.36%. Furthermore, we study the structural features of 32 random polypeptides misclassified as natural ones to unveil any structural similarity to natural proteins. Results show that random proteins misclassified by the ENNA algorithm exhibit a significant fold similarity to portions or subdomains of extant proteins at atomic resolution. Altogether, our results suggest that natural proteins are significantly edited from random polypeptides and evolutionary editing can be readily detected analyzing structural features. Furthermore, we also show that the ENNA, employing simple structural descriptors, can predict whether a protein chain is natural or random

Human genomic regions with exceptionally high levels of population differentiation identified from 911 whole-genome sequences

Data availability: The 1000 Genomes phase I integrated callset used in this study is publicly available at [The 1000 Genomes Project. http://www.1000genomes.org/]. For a list of samples used in this study, refer to Table S1 in Additional file 1. Additional files: Additional file 1: This file contains supplementary Tables ST1 to ST4 (https://static-content.springer.com/esm/art%3A10.1186%2Fgb-2014-15-6-r88/MediaObjects/13059_2014_3364_MOESM1_ESM.xlsx). Additional file 2: This file contains supplementary Figures F1 to F16 (https://static-content.springer.com/esm/art%3A10.1186%2Fgb-2014-15-6-r88/MediaObjects/13059_2014_3364_MOESM2_ESM.pdf). Additional file 3: Full list of participants and institutions in the 1000 Genomes Project (https://static-content.springer.com/esm/art%3A10.1186%2Fgb-2014-15-6-r88/MediaObjects/13059_2014_3364_MOESM3_ESM.pdf).Copyright © 2014 Colonna et al. Background: Population differentiation has proved to be effective for identifying loci under geographically localized positive selection, and has the potential to identify loci subject to balancing selection. We have previously investigated the pattern of genetic differentiation among human populations at 36.8 million genomic variants to identify sites in the genome showing high frequency differences. Here, we extend this dataset to include additional variants, survey sites with low levels of differentiation, and evaluate the extent to which highly differentiated sites are likely to result from selective or other processes. Results: We demonstrate that while sites with low differentiation represent sampling effects rather than balancing selection, sites showing extremely high population differentiation are enriched for positive selection events and that one half may be the result of classic selective sweeps. Among these, we rediscover known examples, where we actually identify the established functional SNP, and discover novel examples including the genes ABCA12, CALD1 and ZNF804, which we speculate may be linked to adaptations in skin, calcium metabolism and defense, respectively. Conclusions: We identify known and many novel candidate regions for geographically restricted positive selection, and suggest several directions for further research.The Wellcome Trust (098051), an Italian National Research Council (CNR) short-term mobility fellowship from the 2013 program to VC, and an EMBO Short Term Fellowship ASTF 324–2010 to VC