Evolutionary Biosemiotics and Multilevel Construction Networks

In contrast to the traditional relational semiotics, biosemiotics decisively deviates towards dynamical aspects of signs at the evolutionary and developmental time scales. The analysis of sign dynamics requires constructivism (in a broad sense) to explain how new components such as subagents, sensors, effectors, and interpretation networks are produced by developing and evolving organisms. Semiotic networks that include signs, tools, and subagents are multilevel, and this feature supports the plasticity, robustness, and evolvability of organisms. The origin of life is described here as the emergence of simple self-constructing semiotic networks that progressively increased the diversity of their components and relations. Primitive organisms have no capacity to classify and track objects; thus, we need to admit the existence of proto-signs that directly regulate activities of agents without being associated with objects. However, object recognition and handling became possible in eukaryotic species with the development of extensive rewritable epigenetic memory as well as sensorial and effector capacities. Semiotic networks are based on sequential and recursive construction, where each step produces components (i.e., agents, scaffolds, signs, and resources) that are needed for the following steps of construction. Construction is not limited to repair and reproduction of what already exists or is unambiguously encoded, it also includes production of new components and behaviors via learning and evolution. A special case is the emergence of new levels of organization known as metasystem transition. Multilevel semiotic networks reshape the phenotype of organisms by combining a mosaic of features developed via learning and evolution of cooperating and/or conflicting subagents.     

Single molecule effects of osteogenesis imperfecta mutations in tropocollagen protein domains

Osteogenesis imperfecta (OI) is a genetic disease characterized by fragile bones, skeletal deformities and, in severe cases, prenatal death that affects more than 1 in 10,000 individuals. Here we show by full atomistic simulation in explicit solvent that OI mutations have a significant influence on the mechanical properties of single tropocollagen molecules, and that the severity of different forms of OI is directly correlated with the reduction of the mechanical stiffness of individual tropocollagen molecules. The reduction of molecular stiffness provides insight into the molecular‐scale mechanisms of the disease. The analysis of the molecular mechanisms reveals that physical parameters of side‐chain volume and hydropathy index of the mutated residue control the loss of mechanical stiffness of individual tropocollagen molecules. We propose a model that enables us to predict the loss of stiffness based on these physical characteristics of mutations. This finding provides an atomistic‐level mechanistic understanding of the role of OI mutations in defining the properties of the basic protein constituents, which could eventually lead to new strategies for diagnosis and treatment the disease. The focus on material properties and their role in genetic diseases is an important, yet so far only little explored, aspect in studying the mechanisms that lead to pathological conditions. The consideration of how material properties change in diseases could lead to a new paradigm that may expand beyond the focus on biochemical readings alone and include a characterization of material properties in diagnosis and treatment, an effort referred to as materiomics.     

Thiosulfate, polythionates and elemental sulfur assimilation and reduction in the bacterial world

Among sulfur compounds, thiosulfate and polythionates are present at least transiently in many environments. These compounds have a similar chemical structure and their metabolism appears closely related. They are commonly used as energy sources for photoautotrophic or chemolithotrophic microorganisms, but their assimilation has been seldom studied and their importance in bacterial physiology is not well understood. Almost all bacterial strains are able to cleave these compounds since they possess thiosulfate sulfur transferase, thiosulfate reductase or S-sulfocysteine synthase activities. However, the role of these enzymes in the assimilation of thiosulfate or polythionates has not always been clearly established. Elemental sulfur is, on the contrary, very common in the environment. It is an energy source for sulfur-reducing eubacteria and archaebacteria and many sulfur-oxidizing archaebacteria. A phenomenon still not well understood is the 'excessive assimilatory sulfur metabolism' as observed in methanogens which perform a sulfur reduction which exceeds their anabolic needs without any apparent benefit. In heterotrophs, assimilation of elemental sulfur is seldom described and it is uncertain whether this process actually has a physiological significance. Thus, reduction of thiosulfate and elemental sulfur is a common but incompletely understood feature among bacteria. These activities could give bacteria a selective advantage, but further investigations are needed to clarify this possibility. Presence of thiosulfate, polythionates and sulfur reductase activities does not imply obligatorily that these activities play a role in thiosulfate, polythionates or sulfur assimilation as these compounds could be merely intermediates in bacterial metabolism. The possibility also exists that the assimilation of these sulfur compounds is just a side effect of an enzymatic activity with a completely different function. As long as these questions remain unanswered, our understanding of sulfur and thiosulfate metabolism will remain incomplete.     

Thiosulfate, polythionates and elemental sulfur assimilation and reduction in the bacterial world

Abstract Among sulfur compounds, thiosulfate and polythionates are present at least transiently in many environments. These compounds have a similar chemical structure and their metabolism appears closely related. They are commonly used as energy sources for photoautotrophic or chemolithotrophic microorganisms, but their assimilation has been seldom studied and their importance in bacterial physiology is not well understood. Almost all bacterial strains are able to cleave these compounds since they possess thiosulfate sulfur transferase, thiosulfate reductace or S -sulfocysteine synthase activities. However, the role of these enzymes in the assimilation of thiosulfate or polythionates has not always been clearly established.
Elemental sulfur is, on the contrary, very common in the environmental. It is an energy source for sulfur-reducing eubacteria and archaebacteria and many sulfur-oxidizing archaebacteria. A phenomenon still not well understood is the 'excessive assimilatory sulfur metabolism' as observed in methanogens which perform a sulfur reduction which exceeds their anabolic needs without any apparent benefit. In heterotrophs, assimilation of elemental sulfur is seldom described and it is uncertain whether this process actually has a physiological significance.
Thus, reduction of thiosulfate and elemental sulfur is a common by incompletely understood feature among bacteria. These activities could give bacteria a selective advantage, but futher investigations are needed to clarify this possibility. Presence of thiosulfate, polythionates and sulfur reductase activities does not imply obligatorily that these activities play a role in thiosulfate, polythionates or sulfur assimilation as these compounds could be merely intermediates in bacterial metabolism. The possibility also exists that the assimilation of these sulfur compounds is just a side effect of an enzymatic activity with a completely different function.     

Impact of Noise on Molecular Network Inference

Molecular entities work in concert as a system and mediate phenotypic outcomes and disease states. There has been recent interest in modelling the associations between molecular entities from their observed expression profiles as networks using a battery of algorithms. These networks have proven to be useful abstractions of the underlying pathways and signalling mechanisms. Noise is ubiquitous in molecular data and can have a pronounced effect on the inferred network. Noise can be an outcome of several factors including: inherent stochastic mechanisms at the molecular level, variation in the abundance of molecules, heterogeneity, sensitivity of the biological assay or measurement artefacts prevalent especially in high-throughput settings. The present study investigates the impact of discrepancies in noise variance on pair-wise dependencies, conditional dependencies and constraint-based Bayesian network structure learning algorithms that incorporate conditional independence tests as a part of the learning process. Popular network motifs and fundamental connections, namely: (a) common-effect, (b) three-chain, and (c) coherent type-I feed-forward loop (FFL) are investigated. The choice of these elementary networks can be attributed to their prevalence across more complex networks. Analytical expressions elucidating the impact of discrepancies in noise variance on pairwise dependencies and conditional dependencies for special cases of these motifs are presented. Subsequently, the impact of noise on two popular constraint-based Bayesian network structure learning algorithms such as Grow-Shrink (GS) and Incremental Association Markov Blanket (IAMB) that implicitly incorporate tests for conditional independence is investigated. Finally, the impact of noise on networks inferred from publicly available single cell molecular expression profiles is investigated. While discrepancies in noise variance are overlooked in routine molecular network inference, the results presented clearly elucidate their non-trivial impact on the conclusions that in turn can challenge the biological significance of the findings. The analytical treatment and arguments presented are generic and not restricted to molecular data sets.     

Structure and regulatory networks of WD40 protein in plants

Plants have been gifted with intricate regulatory networks to carry on with their sessile life form. Often such networks involve delicate association between various proteins. The WD40 proteins, which are present abundantly in several eukaryotes, act as scaffolding molecules assisting proper activity of other proteins. They comprise several stretches of 44–60 amino acid residues and often terminate with a WD dipeptide. They function in several cellular, metabolic and molecular pathways, biologically playing important roles in plant development and also during stress signaling. Moreover, some WD40 (named DWD) proteins also function as substrate receptors in Cullin4 RING dependent E3 ubiquitin ligase mediated proteosomal degradation and DNA damage repair mechanism. In this review, we have discussed the various aspects of these proteins that affect their highly diversified functions in plants.     

Evolutionary origins and directed evolution of RNA

In vitro selection experiments show first and foremost that it is possible that functional nucleic acids can arise from random sequence libraries. Indeed, even simple sequence and structural motifs can prove to be robust binding species and catalysts, indicating that it may have been possible to transition from even the earliest self-replicators to a nascent, RNA-catalyzed metabolism. Because of the diversity of aptamers and ribozymes that can be selected, it is possible to construct a 'fossil record' of the evolution of the RNA world, with in vitro selected catalysts filling in as doppelgangers for molecules long gone. In this way a plausible pathway from simple oligonucleotide replicators to genomic polymerases can be imagined, as can a pathway from basal ribozyme activities to the ribosome. Most importantly, though, in vitro selection experiments can give a true and quantitative idea of the likelihood that these scenarios could have played out in the RNA world. Simple binding species and catalysts could have evolved into other structures and functions. As replicating sequences grew longer, new, more complex functions or faster catalytic activities could have been accessed. Some activities may have been isolated in sequence space, but others could have been approached along large, interconnected neutral networks. As the number, type, and length of ribozymes increased, RNA genomes would have evolved and eventually there would have been no area in a fitness landscape that would have been inaccessible. Self-replication would have inexorably led to life.     

Evidence for the aggregation of hyaluronateprotein fractioned by chromatography on agarose

Bovine vitreous humour was fractionated by gel filtration chromatography on Sepharose 4B into excluded material, presumably of larger molecular size, and retarded material, presumably of smaller molecular size. No evidence of reversible dissociation of the excluded material on treatment with 300 μM L-ascorbic acid at pH 4.00 or pH 7.40 was detected. The retarded material showed evidence of dissociation when eluted with 1.0 M NaCl, and of association in 0.1 M phosphate buffer pH 7.20. These results are compatible with the hypothesis that hyaluronate molecules are synthesises as smaller units which undergo extracellular association to produce larger molecular aggregates in connective tissues.     

The origin of life: RNA and protein co-evolution on the ancient Earth

How life emerged from simple non-life chemicals on the ancient Earth is one of the greatest mysteries in biology. The gene expression system of extant life is based on the interdependence between multiple molecular species (DNA, RNA, and proteins). While DNA is mainly used as genetic material and proteins as functional molecules in modern biology, RNA serves as both genetic material and enzymes (ribozymes). Thus, the evolution of life may have begun with the birth of a ribozyme that replicated itself (the RNA world hypothesis), and proteins and DNA joined later. However, the complete self-replication of ribozymes from monomeric substrates has not yet been demonstrated experimentally, due to their limited activity and stability. In contrast, peptides are more chemically stable and are considered to have existed on the ancient Earth, leading to the hypothesis of RNA–peptide co-evolution from the very beginning. Our group and collaborators recently demonstrated that (1) peptides with both hydrophobic and cationic moieties (e.g., KKVVVVVV) form β-amyloid aggregates that adsorb RNA and enhance RNA synthesis by an artificial RNA polymerase ribozyme and (2) a simple peptide with only seven amino acid types (especially rich in valine and lysine) can fold into the ancient β-barrel conserved in various enzymes, including the core of cellular RNA polymerases. These findings, together with recent reports from other groups, suggest that simple prebiotic peptides could have supported the ancient RNA-based replication system, gradually folded into RNA-binding proteins, and eventually evolved into complex proteins like RNA polymerase.     

Isolation and electron microscopic characterization of the high molecular mass aminoacyl-tRNA synthetase complex from murine erythroleukemia cells

A high molecular mass aminoacyl-tRNA synthetase complex has been isolated from a murine erythroleukemia cell line. This multienzyme complex contains activities for the arginyl-, aspartyl-, glutamyl-, glutaminyl-, isoleucyl,- leucyl-, lysyl-, methionyl-, and prolyl-tRNA synthetases. This enzyme composition, the polypeptide pattern observed upon sodium dodecyl sulfate-polyacrylamide gel electrophoresis, and the relative stoichiometry of the component polypeptides are characteristic of high molecular mass complexes of aminoacyl-tRNA synthetases isolated from a variety of mammalian tissues and cell types. Negatively stained preparations of native complex and of glutaraldehyde-treated material have been examined by electron microscopy. In both cases, a distinctive particle is observed which appears in several orientations. The most common views are of two different projections of a squarish particle that measures approximately 27 x 27 nm. Other commonly observed views are of a "U" shape, a rectangle, and a triangle. All of these views are seen in both gradient-purified samples and those prepared directly from material as isolated. These data are consistent with a model for the multienzyme aminoacyl-tRNA synthetase complex as a "cup" or elongated U structure. These studies demonstrate that the high molecular mass complex of eukaryotic aminoacyl-tRNA synthetases does have a coherent structure that can be visualized by electron microscopy.     

Mutations and Lethality in Simulated Prebiotic Networks

The Graded Autocatalysis Replication Domain (GARD) model describes an origin of life scenario which involves non-covalent compositional assemblies, made of monomeric mutually catalytic molecules. GARD constitutes an alternative to informational biopolymers as a mechanism of primordial inheritance. In the present work, we examined the effect of mutations, one of the most fundamental mechanisms for evolution, in the context of the networks of mutual interaction within GARD prebiotic assemblies. We performed a systematic analysis analogous to single and double gene deletions within GARD. While most deletions have only a small effect on both growth rate and molecular composition of the assemblies, ~10% of the deletions caused lethality, or sometimes showed enhanced fitness. Analysis of 14 different network properties on 2,000 different GARD networks indicated that lethality usually takes place when the deleted node has a high molecular count, or when it is a catalyst for such node. A correlation was also found between lethality and node degree centrality, similar to what is seen in real biological networks. Addressing double knockout mutations, our results demonstrate the occurrence of both synthetic lethality and extragenic suppression within GARD networks, and convey an attempt to correlate synthetic lethality to network node-pair properties. The analyses presented help establish GARD as a workable alternative prebiotic scenario, suggesting that life may have begun with large molecular networks of low fidelity, that later underwent evolutionary compaction and fidelity augmentation.     

Bile acids: From digestion to cancers

Bile acids (BAs) are cholesterol metabolites that have been extensively studied these last decades. BAs have been classified in two groups. Primary BAs are synthesized in liver, when secondary BAs are produced by intestinal bacteria. Recently, next to their ancestral roles in digestion and fat solubilization, BAs have been described as signaling molecules involved in many physiological functions, such as glucose and energy metabolisms. These signaling pathways involve the activation of the nuclear receptor FXRα or of the G-protein-coupled receptor TGR5. These two receptors have selective affinity to different types of BAs and show different expression patterns, leading to different described roles of BAs. It has been suggested for long that BAs could be molecules linked to tumor processes. Indeed, as many other molecules, regarding analyzed tissues, BAs could have either protective or pro-carcinogen activities. However, the molecular mechanisms responsible for these effects have not been characterized yet. It involves either chemical properties or their capacities to activate their specific receptors FXRα or TGR5. This review highlights and discusses the potential links between BAs and cancer diseases and the perspectives of using BAs as potential therapeutic targets in several pathologies.     

Leading and lagging strand synthesis at the replication fork of bacteriophage T7. Distinct properties of T7 gene 4 protein as a helicase and primase

Reactions at the replication fork of bacteriophage T7 have been reconstituted in vitro on a preformed replication fork. A minimum of three proteins is required to catalyze leading and lagging strand synthesis. The T7 gene 4 protein, which exists in two forms of molecular weight 56,000 and 63,000, provides helicase and primase activities. A tight complex of the T7 gene 5 protein and Escherichia coli thioredoxin provides DNA polymerase activity. Gene 4 protein and DNA polymerase catalyze processive leading strand synthesis. Gene 4 protein molecules serving as helicase remain bound to the template as leading strand synthesis proceeds greater than 40 kilobases. Primer synthesis for lagging strand synthesis is catalyzed by additional gene 4 protein molecules that undergo multiple association/dissociation steps to catalyze multiple rounds of primer synthesis. The smaller molecular weight form of gene 4 protein has been purified from an equimolar mixture of both forms. Removal of the large form results in the loss of primase activity but not of helicase activity. Submolar amounts of the large form present in a mixture of both forms are sufficient to restore high specific activity of primase characteristic of an equimolar mixture of both forms. These results suggest that the gene 4 primase is an oligomer which is composed of both molecular weight forms. The large form may be the distributive component of the primase which dissociates from the template after each round of primer synthesis.     

Role of glutamine synthetase activity in the uptake and metabolism of arginine and proline in the cyanobacterium Anabaena cycadeae

Abstract The uptake of arginine and proline and their assimilation as nitrogen source have been studied in the cyanobacterium Anabaena cycadeae and its glutamine auxotropic mutant lacking glutamine synthetase activity. The uptake pattern of arginine and proline was found to be biphasic in both wild-type and mutant strains, consisting of an initial fast phase lasting up to 60 s followed by a slower second phase. The uptake activities of both the amino acids were also found to be similar in both the strains. The wild-type strain, having normal glutamine synthetase activity, utilized arginine and proline as sole nitrogen source, whereas the mutant strain lacking glutamine synthetase activity could not do so. These results suggest that: (1) glutamine synthetase activity is necessarily required for the assimilation of arginine and proline as nitrogen source, but it is not required for the uptake of these amino acids; and (2) glutamine synthetase serves as the sole ammonia-assimilating enzyme as well as glutamine-forming route in heterocystous cyanobacteria.     

Biophysical nanotools for single-molecule dynamics

The focus of the cell biology field is now shifting from characterizing cellular activities to organelle and molecular behaviors. This process accompanies the development of new biophysical visualization techniques that offer high spatial and temporal resolutions with ultra-sensitivity and low cell toxicity. They allow the biology research community to observe dynamic behaviors from scales of single molecules, organelles, cells to organoids, and even live animal tissues. In this review, we summarize these biophysical techniques into two major classes: the mechanical nanotools like dynamic force spectroscopy (DFS) and the optical nanotools like single-molecule and super-resolution microscopy. We also discuss their applications in elucidating molecular dynamics and functionally mapping of interactions between inter-cellular networks and intra-cellular components, which is key to understanding cellular processes such as adhesion, trafficking, inheritance, and division.     

Application of neural networks using the volume learning algorithm for the study of structure-activity relationship in chemical compounds

A volume learning algorithm for artificial neural networks was developed to quantitatively describe the three-dimensional structure-activity relationships using as an example N-benzylpiperidine derivatives. The new algorithm combines two types of neural networks, the Kohonen and the feed-forward artificial neural networks, which are used to analyze the input grid data generated by the comparative molecular field approach. Selection of the most informative parameters using the algorithm helped to reveal the most important spatial properties of the molecules, which affect their biological activities. Cluster regions determined using the new algorithm adequately predicted the activity of molecules from a control data set.     

Foundations of metabolic organization: coherence as a basis of computational properties in metabolic networks

Biological organization is based on the coherent energy transfer allowing for macromolecules to operate with high efficiency and realize computation. Computation is executed with virtually 100% efficiency via the coherent operation of molecular machines in which low-energy recognitions trigger energy-driven non-equilibrium dynamic processes. The recognition process is of quantum mechanical nature being a non-demolition measurement. It underlies the enzymatic conversion of a substrate into the product (an elementary metabolic phenomenon); the switching via separation of the direct and reverse routes in futile cycles provides the generation and complication of metabolic networks (coherence within cycles is maintained by the supramolecular organization of enzymes); the genetic level corresponding to the appearance of digital information is based on reflective arrows (catalysts realize their own self-reproduction) and operation of hypercycles. Every metabolic cycle via reciprocal regulation of both its halves can generate rhythms and spatial structures (resulting from the temporally organized depositions from the cycles). Via coherent events which percolate from the elementary submolecular level to organismic entities, self-assembly based on the molecular complementarity is realized and the dynamic informational field operating within the metabolic network is generated.     

Patterning and polarization of cells by intracellular flows

Beginning with Turing's seminal work [1], decades of research have demonstrated the fundamental ability of biochemical networks to generate and sustain the formation of patterns. However, it is increasingly appreciated that biochemical networks also both shape and are shaped by physical and mechanical processes [2, 3, 4]. One such process is fluid flow. In many respects, the cytoplasm, membrane and actin cortex all function as fluids, and as they flow, they drive bulk transport of molecules throughout the cell. By coupling biochemical activity to long-range molecular transport, flows can shape the distributions of molecules in space. Here, we review the various types of flows that exist in cells, with the aim of highlighting recent advances in our understanding of how flows are generated and how they contribute to intracellular patterning processes, such as the establishment of cell polarity.     

Application of Neural Networks Using the Volume Learning Algorithm for Quantitative Study of the Three-Dimensional Structure–Activity Relationships of Chemical Compounds

A volume learning algorithm for artificial neural networks was developed to quantitatively describe three-dimensional structure–activity relationships using as an example N-benzylpiperidine derivatives. The new algorithm combines two types of neural networks, the Kohonen and the feed-forward artificial neural networks, which are used to analyze the input grid data generated by the comparative molecular field approach. Selection of the most informative parameters using the algorithm helped reveal the most important spatial properties of the molecules, which affect their biological activities. Cluster regions determined using the new algorithm adequately predicted the activity of molecules from a control data set.