On the definition of a self-sustaining chemical reaction system and its role in heredity

Evaluation of sequence-based tools to gather more insight into the positioning of rhizogenic agrobacteria within the Agrobacterium tumefaciens species complex

Rhizogenic Agrobacterium, the causative agent of hairy root disease (HRD), is known for its high phenotypic and genetic diversity. The taxonomy of rhizogenic agrobacteria has undergone several changes in the past and is still somewhat controversial. While the classification of Agrobacterium strains was initially mainly based on phenotypic properties and the symptoms they induced on plants, more and more genetic information has been used along the years to infer Agrobacterium taxonomy. This has led to the definition of the so-called Agrobacterium tumefaciens species complex (Atsc), which comprises several genomospecies. Interestingly, the rhizogenic Agrobacterium strains are found in several of these genomospecies. Nevertheless, even up until today Agrobacterium strains, and in particular rhizogenic agrobacteria, are prone to misclassification and considerable confusion in literature. In this study, we evaluated different phylogenetic analysis approaches for their use to improve Agrobacterium taxonomy and tried to gain more insight in the classification of strains into this complex genus, with a particular focus on rhizogenic agrobacteria. The genome sequence analysis of 580 assemblies, comprising Agrobacterium, Allorhizobium and Rhizobium strains demonstrated that phylogenies based on single marker genes, such as the commonly used 16S rRNA and recA gene, do not provide sufficient resolution for proper delineation of the different genomospecies within the Atsc. Our results revealed that (in silico) multi-locus sequences analysis (MLSA) in combination with average nucleotide identity (ANIb) at a 94.0% threshold delineates genomospecies accurately and efficiently. Additionally, this latter approach permitted the identification of two new candidate genomospecies.

Self-generating autocatalytic networks: structural results, algorithms and their relevance to early biochemistry

The concept of an autocatalytic network of reactions that can form and persist, starting from just an available food source, has been formalized by the notion of a reflexively autocatalytic and food-generated (RAF) set. The theory and algorithmic results concerning RAFs have been applied to a range of settings, from metabolic questions arising at the origin of life, to ecological networks, and cognitive models in cultural evolution. In this article, we present new structural and algorithmic results concerning RAF sets, by studying more complex modes of catalysis that allow certain reactions to require multiple catalysts (or to not require catalysis at all), and discuss the differing ways catalysis has been viewed in the literature. We also focus on the structure and analysis of minimal RAFs and derive structural results and polynomial-time algorithms. We then apply these new methods to a large metabolic network to gain insights into possible biochemical scenarios near the origin of life.

Ladderpath Approach: How Tinkering and Reuse Increase Complexity and Information

The notion of information and complexity are important concepts in many scientific fields such as molecular biology, evolutionary theory and exobiology. Many measures of these quantities are either difficult to compute, rely on the statistical notion of information, or can only be applied to strings. Based on assembly theory, we propose the notion of a ladderpath, which describes how an object can be decomposed into hierarchical structures using repetitive elements. From the ladderpath, two measures naturally emerge: the ladderpath-index and the order-index, which represent two axes of complexity. We show how the ladderpath approach can be applied to both strings and spatial patterns and argue that all systems that undergo evolution can be described as ladderpaths. Further, we discuss possible applications to human language and the origin of life. The ladderpath approach provides an alternative characterization of the information that is contained in a single object (or a system) and could aid in our understanding of evolving systems and the origin of life in particular.

Global mapping of cancers: The Cancer Genome Atlas and beyond

Cancer genomes have been explored from the early 2000s through massive exome sequencing efforts, leading to the publication of The Cancer Genome Atlas in 2013. Sequencing techniques have been developed alongside this project and have allowed scientists to bypass the limitation of costs for whole-genome sequencing (WGS) of single specimens by developing more accurate and extensive cancer sequencing projects, such as deep sequencing of whole genomes and transcriptomic analysis. The Pan-Cancer Analysis of Whole Genomes recently published WGS data from more than 2600 human cancers together with almost 1200 related transcriptomes. The application of WGS on a large database allowed, for the first time in history, a global analysis of features such as molecular signatures, large structural variations and noncoding regions of the genome, as well as the evaluation of RNA alterations in the absence of underlying DNA mutations. The vast amount of data generated still needs to be thoroughly deciphered, and the advent of machine-learning approaches will be the next step towards the generation of personalized approaches for cancer medicine. The present manuscript wants to give a broad perspective on some of the biological evidence derived from the largest sequencing attempts on human cancers so far, discussing advantages and limitations of this approach and its power in the era of machine learning.

Self-reproducing catalytic micelles as nanoscopic protocell precursors

Protocells at life's origin are often conceived as bilayer-enclosed precursors of life, whose self-reproduction rests on the early advent of replicating catalytic biopolymers. This Perspective describes an alternative scenario, wherein reproducing nanoscopic lipid micelles with catalytic capabilities were forerunners of biopolymer-containing protocells. This postulate gains considerable support from experiments describing micellar catalysis and autocatalytic proliferation, and, more recently, from reports on cross-catalysis in mixed micelles that lead to life-like steady-state dynamics. Such results, along with evidence for micellar prebiotic compatibility, synergize with predictions of our chemically stringent computer-simulated model, illustrating how mutually catalytic lipid networks may enable micellar compositional reproduction that could underlie primal selection and evolution. Finally, we highlight studies on how endogenously catalysed lipid modifications could guide further protocellular complexification, including micelle to vesicle transition and monomer to biopolymer progression. These portrayals substantiate the possibility that protocellular evolution could have been seeded by pre-RNA lipid assemblies.