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.     

Identifying essential genes in bacterial metabolic networks with machine learning methods

Background  

Identifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective.     

Trees on networks: resolving statistical patterns of phylogenetic similarities among interacting proteins

Background  

Phylogenies capture the evolutionary ancestry linking extant species. Correlations and similarities among a set of species are mediated by and need to be understood in terms of the phylogenic tree. In a similar way it has been argued that biological networks also induce correlations among sets of interacting genes or their protein products.     

Computational and Experimental Investigation of the Antimicrobial Peptide Cecropin XJ and its Ligands as the Impact Factors of Antibacterial Activity

Cecropin XJ, as a heat stable antimicrobial peptide (AMP), displayed broad bacteriostatic activities, effectively inhibited proliferation of cancer cells and induced cell apoptosis in vitro. However, it exhibited little hemolytic activity and very low cytotoxicity to erythrocytes and normal cells. Although exerts multiple remarkable bioactivities, the refined molecular conformation of native Cecropin XJ remains unsolved. The aim of the present study is to comprehensively investigate the physicochemical characteristics and structure-function relationship of this antimicrobial peptide by using a series of bioinformatics and experimental approaches. In this study, we revealed that the mature Cecropin XJ consists of 41 amino acids, containing two α-helical structures from Lys⁷ to Lys²⁵ and from Ala²⁹ to Ile³⁹. The phylogenetic tree indicated that Cecropin XJ belongs to the Class I AMPs of cecropin family. Hydrophobic analysis showed Cecropin XJ is a typical amphiphilic molecule. The surface of Cecropin XJ was found to have a much wide range of electrostatic potential from ?83.243 to +83.243. The amphipathicity and surface potential of Cecropin XJ partially supported the AMP pore-forming hypothesis. Scanning electron microscopy experimentally confirmed the damages of Cecropin XJ to microbial membrane. Four predicted docking sites respectively for magnesium ion (Mg²⁺), adenosine diphosphate (ADP), bacteriopheophytin (BPH), and guanosine triphosphate (GTP) were found on the surface of Cecropin XJ. Thereinto, Mg²⁺ was experimentally proved to suppress the antibacterial activity of Cecropin XJ; both GTP and ADP enhanced the bactericidal activities to varying degrees. The present study provides a foundation for further investigation of molecular evolution, structural modification, and functional mechanisms of Cecropin XJ.     

Parameter estimation for the distribution of single cell lag times

In Quantitative Microbial Risk Assessment, it is vital to understand how lag times of individual cells are distributed over a bacterial population. Such identified distributions can be used to predict the time by which, in a growth-supporting environment, a few pathogenic cells can multiply to a poisoning concentration level.We model the lag time of a single cell, inoculated into a new environment, by the delay of the growth function characterizing the generated subpopulation. We introduce an easy-to-implement procedure, based on the method of moments, to estimate the parameters of the distribution of single cell lag times. The advantage of the method is especially apparent for cases where the initial number of cells is small and random, and the culture is detectable only in the exponential growth phase.