Non-equilibrium conditions inside rock pores drive fission, maintenance and selection of coacervate protocells

Key requirements for the first cells on Earth include the ability to compartmentalize and evolve. Compartmentalization spatially localizes biomolecules from a dilute pool and an evolving cell, which, as it grows and divides, permits mixing and propagation of information to daughter cells. Complex coacervate microdroplets are excellent candidates as primordial cells with the ability to partition and concentrate molecules into their core and support primitive and complex biochemical reactions. However, the evolution of coacervate protocells by fusion, growth and fission has not yet been demonstrated. In this work, a primordial environment initiated the evolution of coacervate-based protocells. Gas bubbles inside heated rock pores perturb the coacervate protocell distribution and drive the growth, fusion, division and selection of coacervate microdroplets. Our findings provide a compelling scenario for the evolution of membrane-free coacervate microdroplets on the early Earth, induced by common gas bubbles within heated rock pores.

Leveraging biochemical reactions to unravel functional impacts of cancer somatic variants affecting protein interaction interfaces

Background: Considering protein mutations in their biological context is essential for understanding their functional impact, interpretation of high-dimensional datasets and development of effective targeted therapies in personalized medicine. Methods: We combined the curated knowledge of biochemical reactions from Reactome with the analysis of interaction-mediating 3D interfaces from Mechismo. In addition, we provided a software tool for users to explore and browse the analysis results in a multi-scale perspective starting from pathways and reactions to protein-protein interactions and protein 3D structures. Results: We analyzed somatic mutations from TCGA, revealing several significantly impacted reactions and pathways in specific cancer types. We found examples of genes not yet listed as oncodrivers, whose rare mutations were predicted to affect cancer processes similarly to known oncodrivers. Some identified processes lack any known oncodrivers, which suggests potentially new cancer-related processes (e.g. complement cascade reactions). Furthermore, we found that mutations perturbing certain processes are significantly associated with distinct phenotypes (i.e. survival time) in specific cancer types (e.g. PIK3CA centered pathways in LGG and UCEC cancer types), suggesting the translational potential of our approach for patient stratification. Our analysis also uncovered several druggable processes (e.g. GPCR signalling pathways) containing enriched reactions, providing support for new off-label therapeutic options. Conclusions: In summary, we have established a multi-scale approach to study genetic variants based on protein-protein interaction 3D structures. Our approach is different from previously published studies in its focus on biochemical reactions and can be applied to other data types (e.g. post-translational modifications) collected for many types of disease.

Uncovering the Role of Metabolism in Oomycete–Host Interactions Using Genome-Scale Metabolic Models

Metabolism is the set of biochemical reactions of an organism that enables it to assimilate nutrients from its environment and to generate building blocks for growth and proliferation. It forms a complex network that is intertwined with the many molecular and cellular processes that take place within cells. Systems biology aims to capture the complexity of cells, organisms, or communities by reconstructing models based on information gathered by high-throughput analyses (omics data) and prior knowledge. One type of model is a genome-scale metabolic model (GEM) that allows studying the distributions of metabolic fluxes, i.e., the “mass-flow” through the network of biochemical reactions. GEMs are nowadays widely applied and have been reconstructed for various microbial pathogens, either in a free-living state or in interaction with their hosts, with the aim to gain insight into mechanisms of pathogenicity. In this review, we first introduce the principles of systems biology and GEMs. We then describe how metabolic modeling can contribute to unraveling microbial pathogenesis and host–pathogen interactions, with a specific focus on oomycete plant pathogens and in particular Phytophthora infestans. Subsequently, we review achievements obtained so far and identify and discuss potential pitfalls of current models. Finally, we propose a workflow for reconstructing high-quality GEMs and elaborate on the resources needed to advance a system biology approach aimed at untangling the intimate interactions between plants and pathogens.

Dynamische Peptidvesikel als Vorstufe synthetischer Zellen?

Compartmentalization and spatial control of biochemical reactions are essential for the creation of artificial cellular systems. But it is notoriously difficult to implement reaction networks and membrane building blocks in vitro. In this article, we briefly highlight our work on the formation of peptide-based vesicles as artificial cell precursors with a size of up to 100 µm These cell mimics are able to produce RNA aptamers and proteins at the inside, and are further capable of strong growth.

Relatedness and the evolution of mechanisms to divide labour in microorganisms

Division of labour occurs when cooperating individuals specialise to perform different tasks. In bacteria and other microorganisms, some species divide labour by random specialisation, where an individual’s role is determined by random fluctuations in biochemical reactions within the cell. Other species divide labour by coordinating across individuals to determine which cells will perform which task, using mechanisms such as between-cell signalling. However, previous theory, examining the evolution of mechanisms to divide labour between reproductives and sterile helpers, has only considered clonal populations, where there is no potential for conflict between individuals. We used a mixture of analytical and simulation models to examine non-clonal populations and found that: (1) intermediate levels of coordination can be favoured, between the extreme of no coordination (random) and full coordination; (2) as relatedness decreases, coordinated division of labour is less likely to be favoured. Our results can help explain why coordinated division of labour is relatively rare in bacteria, where groups may frequently be non-clonal.

Computational Modeling and Evolutionary Implications of Biochemical Reactions in Bacterial Microcompartments

Bacterial microcompartments (BMCs) are protein-encapsulated compartments found across at least 23 bacterial phyla. BMCs contain a variety of metabolic processes that share the commonality of toxic or volatile intermediates, oxygen-sensitive enzymes and cofactors, or increased substrate concentration for magnified reaction rates. These compartmentalized reactions have been computationally modeled to explore the encapsulated dynamics, ask evolutionary-based questions, and develop a more systematic understanding required for the engineering of novel BMCs. Many crucial aspects of these systems remain unknown or unmeasured, such as substrate permeabilities across the protein shell, feasibility of pH gradients, and transport rates of associated substrates into the cell. This review explores existing BMC models, dominated in the literature by cyanobacterial carboxysomes, and highlights potentially important areas for exploration.