The molecular roots of compositional inheritance.

Non-covalent compositional assemblies, made of monomeric mutually catalytic molecules, constitute an alternative to alphabet-based informational biopolymers as a mechanism of primordial inheritance. Such assemblies appear implicitly in many "Metabolism First" origin of life scenarios, and more explicitly in the Graded Autocatalysis Replication Domain (GARD) model [Segréet al. (2000). Proc. Natl Acad. Sci. U.S.A.97, 4112-4117]. In the present work, we provide a detailed analysis of the quantitative molecular roots of such behavior. It is demonstrated that the fidelity of reproduction provided by a newly defined heritability measure eta(*)(s), strongly depends on the values of molecular recognition parameters and on assembly size. We find that if the catalytic rate acceleration coefficients are distributed normally, transfer of compositional information becomes impossible, due to frequent "compositional error catastrophes". In contrast, if the catalytic acceleration rates obey a lognormal distribution, as actually predicted by a statistical formalism for molecular repertoires, high reproduction fidelity is obtained. There is also a clear dependence on assembly size N, whereby maximal eta is seen in a narrow range around N approximately 3.5 N(G)/lambda, where N(G)is the size of the primordial molecular repertoire and lambda is a molecular interaction statistical parameter. Such relationships help define the physicochemical conditions that could underlie the early steps in pre-biotic evolution.     

Coevolution of compositional protocells and their environment

The coevolution of environment and living organisms is well known in nature. Here, it is suggested that similar processes can take place before the onset of life, where protocellular entities, rather than full-fledged living systems, coevolve along with their surroundings. Specifically, it is suggested that the chemical composition of the environment may have governed the chemical repertoire generated within molecular assemblies, compositional protocells, while compounds generated within these protocells altered the chemical composition of the environment. We present an extension of the graded autocatalysis replication domain (GARD) model--the environment exchange polymer GARD (EE-GARD) model. In the new model, molecules, which are formed in a protocellular assembly, may be exported to the environment that surrounds the protocell. Computer simulations of the model using an infinite-sized environment showed that EE-GARD assemblies may assume several distinct quasi-stationary compositions (composomes), similar to the observations in previous variants of the GARD model. A statistical analysis suggested that the repertoire of composomes manifested by the assemblies is independent of time. In simulations with a finite environment, this was not the case. Composomes, which were frequent in the early stages of the simulation disappeared, while others emerged. The change in the frequencies of composomes was found to be correlated with changes induced on the environment by the assembly. The EE-GARD model is the first GARD model to portray a possible time evolution of the composomes repertoire.     

Compositional complementarity and prebiotic ecology in the origin of life

We hypothesize that life began not with the first self-reproducing molecule or metabolic network, but as a prebiotic ecology of co-evolving populations of macromolecular aggregates (composomes). Each composome species had a particular molecular composition resulting from molecular complementarity among environmentally available prebiotic compounds. Natural selection acted on composomal species that varied in properties and functions such as stability, catalysis, fission, fusion and selective accumulation of molecules from solution. Fission permitted molecular replication based on composition rather than linear structure, while fusion created composomal variability. Catalytic functions provided additional chemical novelty resulting eventually in autocatalytic and mutually catalytic networks within composomal species. Composomal autocatalysis and interdependence allowed the Darwinian co-evolution of content and control (metabolism). The existence of chemical interfaces within complex composomes created linear templates upon which self-reproducing molecules (such as RNA) could be synthesized, permitting the evolution of informational replication by molecular templating. Mathematical and experimental tests are proposed.     

Prospects of a Computational Origin of Life Endeavor

While the last century brought an exquisite understanding of the molecular basis of life, very little is known about the detailed chemical mechanisms that afforded the emergence of life on early earth. There is a broad agreement that the problem lies in the realm of chemistry, and likely resides in the formation and mutual interactions of carbon-based molecules in aqueous medium. Yet, present-day experimental approaches can only capture the synthesis and behavior of a few molecule types at a time. On the other hand, experimental simulations of prebiotic syntheses, as well as chemical analyses of carbonaceous meteorites, suggest that the early prebiotic hydrosphere contained many thousands of different compounds. The present paper explores the idea that given the limitations of test-tube approaches with regards to such a 'random chemistry' scenario, an alternative mode of analysis should be pursued. It is argued that as computational tools for the reconstruction of molecular interactions improve rapidly, it may soon become possible to perform adequate computer-based simulations of prebiotic evolution. We thus propose to launch a computational origin of life endeavor (http://ool.weizmann.ac.il/CORE), involving computer simulations of realistic complex prebiotic chemical networks. In the present paper we provide specific examples, based on a novel algorithmic approach, which constitutes a hybrid of molecular dynamics and stochastic chemistry. As one potential solution for the immense hardware requirements dictated by this approach, we have begun to implement an idle CPU harvesting scheme, under the title ool@home.     

dSLAM analysis of genome-wide genetic interactions in Saccharomyces cerevisiae

Analysis of genetic interactions has been extensively exploited to study gene functions and to dissect pathway structures. One such genetic interaction is synthetic lethality, in which the combination of two non-lethal mutations leads to loss of organism viability. We have developed a dSLAM (heterozygote diploid-based synthetic lethality analysis with microarrays) technology that effectively studies synthetic lethality interactions on a genome-wide scale in the budding yeast Saccharomyces cerevisiae. Typically, a query mutation is introduced en masse into a population of approximately 6000 haploid-convertible heterozygote diploid Yeast Knockout (YKO) mutants via integrative transformation. Haploid pools of single and double mutants are freshly generated from the resultant heterozygote diploid double mutant pool after meiosis and haploid selection and studied for potential growth defects of each double mutant combination by microarray analysis of the "molecular barcodes" representing each YKO. This technology has been effectively adapted to study other types of genome-wide genetic interactions including gene-compound synthetic lethality, secondary mutation suppression, dosage-dependent synthetic lethality and suppression.     

Creating Prebiotic Sanctuary: Self-Assembling Supramolecular Peptide Structures Bind and Stabilize RNA

Any attempt to uncover the origins of life must tackle the known ‘blind watchmaker problem’. That is to demonstrate the likelihood of the emergence of a prebiotic system simple enough to be formed spontaneously and yet complex enough to allow natural selection that will lead to Darwinistic evolution. Studies of short aromatic peptides revealed their ability to self-assemble into ordered and stable structures. The unique physical and chemical characteristics of these peptide assemblies point out to their possible role in the origins of life. We have explored mechanisms by which self-assembling short peptides and RNA fragments could interact together and go through a molecular co-evolution, using diphenylalanine supramolecular assemblies as a model system. The spontaneous formation of these self-assembling peptides under prebiotic conditions, through the salt-induced peptide formation (SIPF) pathway was demonstrated. These peptide assemblies possess the ability to bind and stabilize ribonucleotides in a sequence-depended manner, thus increase their relative fitness. The formation of these peptide assemblies is dependent on the homochirality of the peptide monomers: while homochiral peptides (L-Phe-L-Phe and D-Phe-D-Phe) self-assemble rapidly in aqueous environment, heterochiral diastereoisomers (L-Phe-D-Phe and D-Phe-L-Phe) do not tend to self-assemble. This characteristic consists with the homochirality of all living matter. Finally, based on these findings, we propose a model for the role of short self-assembling peptides in the prebiotic molecular evolution and the origin of life.     

Functional essentiality from topology features in metabolic networks: a case study in yeast

The relation between the position of mutations in Saccharomyces cerevisiae metabolic network and their lethality is the subject of this work. We represent the topology of the network by a directed graph: nodes are metabolites and arcs represent the reactions; a mutation corresponds to the removal of all the arcs referring to the deleted enzyme. Using publicly available knock-out data, we show that lethality corresponds to the lack of alternative paths in the perturbed network linking the nodes affected by the enzyme deletion. Such feature is at the basis of the recently recognized importance of 'marginal' arcs of metabolic networks.     

The origin of life on Earth

Terrestrial life can be schematically described as organic molecules organized in liquid water. According to Oparin's hypothesis, organic building blocks required for early life were produced from simple organic molecules formed in a primitive reducing atmosphere. Precursors of lipids, nucleic acids and enzymes obtained in the laboratory under simulating conditions are reviewed. Geochemists favor now a less reducing atmosphere dominated by carbon dioxide. In such an atmosphere, very few building blocks are formed under prebiotic conditions. Import of extraterrestrial organic molecules may represent an alternative supply. Experimental support for such an alternative scenario is examined in comets, cosmic dust, meteorites and micrometeorites. Even the prebiotic broth receives today severe criticism for being implausible. In contrast to the classical scenario, a chemoautotrophic origin of life is discussed. Finally, interesting information related to early terrestrial life may be gained from Mars exploration.     

Prediction of lethal and synthetically lethal knock-outs in regulatory networks

The complex interactions involved in regulation of a cell’s function are captured by its interaction graph. More often than not, detailed knowledge about enhancing or suppressive regulatory influences and cooperative effects is lacking and merely the presence or absence of directed interactions is known. Here, we investigate to which extent such reduced information allows to forecast the effect of a knock-out or a combination of knock-outs. Specifically, we ask in how far the lethality of eliminating nodes may be predicted by their network centrality, such as degree and betweenness, without knowing the function of the system. The function is taken as the ability to reproduce a fixed point under a discrete Boolean dynamics. We investigate two types of stochastically generated networks: fully random networks and structures grown with a mechanism of node duplication and subsequent divergence of interactions. On all networks we find that the out-degree is a good predictor of the lethality of a single node knock-out. For knock-outs of node pairs, the fraction of successors shared between the two knocked-out nodes (out-overlap) is a good predictor of synthetic lethality. Out-degree and out-overlap are locally defined and computationally simple centrality measures that provide a predictive power close to the optimal predictor.     

Molecular Semiotics toward the Emergence of Life

Molecular imprints of organisms serving as both the agents and the products of the underlying sign activities are quantum mechanical in their origins. In particular, molecules in any reaction networks constituting a biological organism are semiotic or context-dependent in the sense that their activities reside within the proper coordination of the entire networks. The origin of life could have been related to a specific aspect of molecular semiotics, especially in the transition from molecules as the physical symbols of material units to molecules as the semiotic signs having the capacity of pointing to something else other than the molecules themselves. Quantum mechanical underpinning of the molecular imprints leading to the emergence of life is in the appraisal of the material capacities of both coherent assimilation and decoherent dissociation already latent in the imprints. One empirical evidence suggesting the likelihood of both coherent assimilation and decoherent dissociation in prebiotic settings could have been found in synthetic chemical reactions running in hydrothermal circulation of seawater through hot vents in the Haedean ocean on the primitive Earth.     

Polymer Gard: Computer Simulation of Covalent Bond Formation in Reproducing Molecular Assemblies

The basic Graded Autocatalysis Replication Domain (GARD) model consists of a repertoire of small molecules, typically amphiphiles, which join and leave a non-covalent micelle-like assembly. Its replication behavior is due to occasional fission, followed by a homeostatic growth process governed by the assembly’ s composition. Limitations of the basic GARD model are its small finite molecular repertoire and the lack of a clear path from a ‘monomer world’ towards polymer-based living entities.We have now devised an extension of the model (polymer GARD or P-GARD), where a monomer-based GARD serves as a ‘scaffold’ for oligomer formation, as a result of internal chemical rules. We tested this concept with computer simulations of a simple case of monovalent monomers, whereby more complex molecules (dimers) are formed internally, in a manner resembling biosynthetic metabolism. We have observed events of dimer ‘take-over’ – the formation of compositionally stable, replication-prone quasi stationary states (composomes) that have appreciable dimer content. The appearance of novel metabolism-like networks obeys a time-dependent power law, reminiscent of evolution under punctuated equilibrium. A simulation under constant population conditions shows the dynamics of takeover and extinction of different composomes, leading to the generation of different population distributions. The P-GARD model offers a scenario whereby biopolymer formation may be a result of rather than a prerequisite for early life-like processes.     

Three-dimensional reconstruction of protein networks provides insight into human genetic disease

To better understand the molecular mechanisms and genetic basis of human disease, we systematically examine relationships between 3,949 genes, 62,663 mutations and 3,453 associated disorders by generating a three-dimensional, structurally resolved human interactome. This network consists of 4,222 high-quality binary protein-protein interactions with their atomic-resolution interfaces. We find that in-frame mutations (missense point mutations and in-frame insertions and deletions) are enriched on the interaction interfaces of proteins associated with the corresponding disorders, and that the disease specificity for different mutations of the same gene can be explained by their location within an interface. We also predict 292 candidate genes for 694 unknown disease-to-gene associations with proposed molecular mechanism hypotheses. This work indicates that knowledge of how in-frame disease mutations alter specific interactions is critical to understanding pathogenesis. Structurally resolved interaction networks should be valuable tools for interpreting the wealth of data being generated by large-scale structural genomics and disease association studies.     

Fidelity of replication of repetitive DNA in mutS and repair proficient Escherichia coli

Replication fidelity is not constant among strains within a species or at all genetic loci within a genome. Altered fidelity of replication may affect patterns of pathogenesis and the evolution of these strains. We have been studying replication fidelity in Escherichia coli, both in laboratory attenuated strains and in food-borne pathogens. To understand the altered patterns of mutagenesis at the molecular level, we used a shuttle vector plasmid with a tRNA mutational marker gene which had been altered to include homopolymeric runs of five, seven and nine [G:C] pairs, as well as non-repetitive DNA. Replication of the plasmid in mutS strains resulted in a 20-fold increase in mutant progeny plasmids. The mutations were almost all (>90%) frameshift mutations, while base substitution mutations were rare. Most mutations were insertions or deletions of one or two [G:C] pairs in the longest homopolymeric runs. Larger deletions (5 to >70bp), also targeted to the repetitive sequence, were likewise common. Mutations increased exponentially with the length of the homopolymeric run. These patterns of mutation, including unexpectedly high levels in repair proficient strains, led to an examination of the E. coli K-12 genome for homopolymeric DNA. This sequence motif was found to be rare, particularly in genes and open reading frames. Amino acid homotrimers were found to avoid usage of homopolymeric codons, even when they are preferred among synonymous codons in E. coli. There appears to be active selection against tandem direct nucleotide repeats in the E. coli genome, correlated with the inability of the organism to accurately replicate such sequence.     

Predicting genetic interactions with random walks on biological networks

Background  

Several studies have demonstrated that synthetic lethal genetic interactions between gene mutations provide an indication of functional redundancy between molecular complexes and pathways. These observations help explain the finding that organisms are able to tolerate single gene deletions for a large majority of genes. For example, system-wide gene knockout/knockdown studies in S. cerevisiae and C. elegans revealed non-viable phenotypes for a mere 18% and 10% of the genome, respectively. It has been postulated that the low percentage of essential genes reflects the extensive amount of genetic buffering that occurs within genomes. Consistent with this hypothesis, systematic double-knockout screens in S. cerevisiae and C. elegans show that, on average, 0.5% of tested gene pairs are synthetic sick or synthetic lethal. While knowledge of synthetic lethal interactions provides valuable insight into molecular functionality, testing all combinations of gene pairs represents a daunting task for molecular biologists, as the combinatorial nature of these relationships imposes a large experimental burden. Still, the task of mapping pairwise interactions between genes is essential to discovering functional relationships between molecular complexes and pathways, as they form the basis of genetic robustness. Towards the goal of alleviating the experimental workload, computational techniques that accurately predict genetic interactions can potentially aid in targeting the most likely candidate interactions. Building on previous studies that analyzed properties of network topology to predict genetic interactions, we apply random walks on biological networks to accurately predict pairwise genetic interactions. Furthermore, we incorporate all published non-interactions into our algorithm for measuring the topological relatedness between two genes. We apply our method to S. cerevisiae and C. elegans datasets and, using a decision tree classifier, integrate diverse biological networks and show that our method outperforms established methods.     

Edgetic perturbation models of human inherited disorders

Cellular functions are mediated through complex systems of macromolecules and metabolites linked through biochemical and physical interactions, represented in interactome models as ‘nodes’ and ‘edges’, respectively. Better understanding of genotype‐to‐phenotype relationships in human disease will require modeling of how disease‐causing mutations affect systems or interactome properties. Here we investigate how perturbations of interactome networks may differ between complete loss of gene products (‘node removal’) and interaction‐specific or edge‐specific (‘edgetic’) alterations. Global computational analyses of ～50 000 known causative mutations in human Mendelian disorders revealed clear separations of mutations probably corresponding to those of node removal versus edgetic perturbations. Experimental characterization of mutant alleles in various disorders identified diverse edgetic interaction profiles of mutant proteins, which correlated with distinct structural properties of disease proteins and disease mechanisms. Edgetic perturbations seem to confer distinct functional consequences from node removal because a large fraction of cases in which a single gene is linked to multiple disorders can be modeled by distinguishing edgetic network perturbations. Edgetic network perturbation models might improve both the understanding of dissemination of disease alleles in human populations and the development of molecular therapeutic strategies.     

Emergence of Slow-Switching Assemblies in Structured Neuronal Networks

Unraveling the interplay between connectivity and spatio-temporal dynamics in neuronal networks is a key step to advance our understanding of neuronal information processing. Here we investigate how particular features of network connectivity underpin the propensity of neural networks to generate slow-switching assembly (SSA) dynamics, i.e., sustained epochs of increased firing within assemblies of neurons which transition slowly between different assemblies throughout the network. We show that the emergence of SSA activity is linked to spectral properties of the asymmetric synaptic weight matrix. In particular, the leading eigenvalues that dictate the slow dynamics exhibit a gap with respect to the bulk of the spectrum, and the associated Schur vectors exhibit a measure of block-localization on groups of neurons, thus resulting in coherent dynamical activity on those groups. Through simple rate models, we gain analytical understanding of the origin and importance of the spectral gap, and use these insights to develop new network topologies with alternative connectivity paradigms which also display SSA activity. Specifically, SSA dynamics involving excitatory and inhibitory neurons can be achieved by modifying the connectivity patterns between both types of neurons. We also show that SSA activity can occur at multiple timescales reflecting a hierarchy in the connectivity, and demonstrate the emergence of SSA in small-world like networks. Our work provides a step towards understanding how network structure (uncovered through advancements in neuroanatomy and connectomics) can impact on spatio-temporal neural activity and constrain the resulting dynamics.     

Detection of Macromolecular Fractions in HCN Polymers Using Electrophoretic and Ultrafiltration Techniques

Elucidating the origin of life involves synthetic as well as analytical challenges. Herein, for the first time, we describe the use of gel electrophoresis and ultrafiltration to fractionate HCN polymers. Since the first prebiotic synthesis of adenine by Oró, HCN polymers have gained much interest in studies on the origins of life due to the identification of biomonomers and related compounds within them. Here, we demonstrate that macromolecular fractions with electrophoretic mobility can also be detected within HCN polymers. The migration of polymers under the influence of an electric field depends not only on their sizes (one‐dimensional electrophoresis) but also their different isoelectric points (two‐dimensional electrophoresis, 2‐DE). The same behaviour was observed for several macromolecular fractions detected in HCN polymers. Macromolecular fractions with apparent molecular weights as high as 250 kDa were detected by tricine‐SDS gel electrophoresis. Cationic macromolecular fractions with apparent molecular weights as high as 140 kDa were also detected by 2‐DE. The HCN polymers synthesized were fractionated by ultrafiltration. As a result, the molecular weight distributions of the macromolecular fractions detected in the HCN polymers directly depended on the synthetic conditions used to produce these polymers. The implications of these results for prebiotic chemistry will be discussed.     

Molecular spectra of HPRT deletion mutations in circulating T-lymphocytes in Fanconi anemia patients

The principal cellular feature of Fanconi anemia (FA), an inherited cancer prone disorder, is a high level of chromosomal breakage, amplified after treatment with crosslinking agents. Three of the eight genes involved in FA have been cloned: FANCA, FANCC and FANCG. However, their biological functions remain unknown. We previously observed an excessive production of deletions at the HPRT locus in FA lymphoblasts belonging to the relatively rare complementation group D(1) and an increased frequency of glycophorin A (GPA) variants in erythrocytes derived from FA patients (2). In thi study, we examined the molecular nature of 31 HPRT mutations formed in vivo in circulating T-lymphocytes isolated from 9 FA male patients. The results show that in all FA patients investigated the deletions are by far the most prevalent mutational event in contrast to age matched healthy donors, in which point mutations predominate. The complementation group in the FA patients examined in the present study has not yet been defined. However, knowing that mutations in the FANCA and FANCC gene are found to be involved in at least 70% of the FA patients, it can be expected that the excessive production of deletions is a general feature of the FA phenotype. In addition, the spectrum of HPRT deletions observed in FA patients differs from that of healthy children: there is a high frequency of 3'-terminal deletions and a strikingly low proportion of V(D)J mediated events. Based on previous findings, a decreased fidelity of coding V(D)J joint formation (3) and an inaccurate repair of specific DNA double strand breaks via Non-Homologous End Joining (4), we propose that FA genes play a role in the control of the fidelity of rejoining of specific DNA ends. Such a defect may explain several basic features of FA, such as chromosomal instability and deletion pronenness.     

Surfactant Assemblies and their Various Possible Roles for the Origin(S) of Life

A large number of surfactants (surface active molecules) are chemically simple compounds that can be obtained by simple chemical reactions, in some cases even under presumably prebiotic conditions. Surfactant assemblies are self-organized polymolecular aggregates of surfactants, in the simplest case micelles, vesicles, hexagonal and cubic phases. It may be that these different types of surfactant assemblies have played various, so-far underestimated important roles in the processes that led to the formation of the first living systems.Although nucleic acids are key players in the formation of cells as we know them today (RNA world hypothesis), it is still unclear how RNA could have been formed under prebiotic conditions. Surfactants with their self-organizing properties may have assisted, controlled and compartimentalized some of the chemical reactions that eventually led to the formation of molecules like RNA. Therefore, surfactants were possibly very important in prebiotic times in the sense that they may have been involved in different physical and chemical processes that finally led to a transformation of non-living matter to the first cellular form(s) of life. This hypothesis is based on four main experimental observations: (i) Surfactant aggregation can lead to cell-like compartimentation (vesicles). (ii) Surfactant assemblies can provide local reaction conditions that are very different from the bulk medium, which may lead to a dramatic change in the rate of chemical reactions and to a change in reaction product distributions. (iii) The surface properties of surfactant assemblies that may be liquid- or solid-like, charged or neutral, and the elasticity and packing density of surfactant assemblies depend on the chemical structure of the surfactants, on the presence of other molecules, and on the overall environmental conditions (e. g. temperature). This wide range of surface characteristics of surfactant assemblies may allow a control of surface-bound chemical reactions not only by the charge or hydrophobicity of the surface but also by its “softness”. (iv) Chiral polymolecular assemblies (helices) may form from chiral surfactants.There are many examples that illustrate the different roles and potential roles of surfactant assemblies in different research areas outside of the field of the origin(s) of life, most importantly in investigations of contemporary living systems, in nanotechnology applications, and in the development of drug delivery systems. Concepts and ideas behind many of these applications may have relevance also in connection to the different unsolved problems in understanding the origin(s) of life.     

Is There an Optimal Level of Open-Endedness in Prebiotic Evolution?

In this paper we explore the question of whether there is an optimal set up for a putative prebiotic system leading to open-ended evolution (OEE) of the events unfolding within this system. We do so by proposing two key innovations. First, we introduce a new index that measures OEE as a function of the likelihood of events unfolding within a universe given its initial conditions. Next, we apply this index to a variant of the graded autocatalysis replication domain (GARD) model, Segre et al. (P Natl Acad Sci USA 97(8):4112-4117, 2000; Markovitch and Lancet Artif Life 18(3), 2012), and use it to study - under a unified and concise prebiotic evolutionary framework - both a variety of initial conditions of the universe and the OEE of species that evolve from them.