From chemical metabolism to life: the origin of the genetic coding process

Cotton metabolism regulatory network: Unraveling key genes and pathways in fiber development and growth regulation

Cotton (Gossypium hirsutum L.) is one of the world's most important commercial crops. However, the dynamics of metabolite abundance and potential regulatory networks throughout its life cycle remain poorly understood. In this study, we developed a cotton metabolism regulatory network (CMRN) that spans various developmental stages and encompasses 2138 metabolites and 90 309 expressed genesin upland cotton. By integrating high-resolution spatiotemporal metabolome and transcriptome data, we identified 1958 differentially accumulated metabolites and 13 597 co-expressed differentially expressed genes between the dwarf mutant pagoda1 and its wild-type counterpart Zhongmiansuo 24. These metabolites and genes were categorized into seven clusters based on tissue-specific accumulation patterns and gene expression profiles across different developmental stages. Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed significant differential enrichment in the fatty acid elongation pathway, particularly in fibers. The differential involvement of genes and metabolites in very-long-chain fatty acid (VLCFA) synthesis led to the identification of GhKCS1b_Dt as a key gene. Overexpression of GhKCS1b_Dt significantly promoted fiber elongation, while its silencing markedly inhibited cotton fiber growth, affirming its positive regulatory role in fiber elongation. This dataset provides a valuable resource for further research into metabolic pathways and gene regulatory networks, offering novel insights for advancing cotton breeding strategies.

Is Darwinian selection a retrograde driving force of evolution?

Modern science has still not provided a satisfactory empirical explanation for the increasing complexity of living organisms through evolutionary history. As no agreed-upon definitions of the complexity exist, the working definition of biological complexity has been formulated. There is no theoretical reason to expect evolutionary lineages to increase in complexity over time, and there is no empirical evidence that they do so. In our discussion we have assumed the hypothesis that at the origins of life, evolution had to first involve autocatalytic systems that only subsequently acquired the capacity of genetic heredity. We discuss the role of Darwinian selection in evolution and pose the hypothesis that Darwinian selection acts predominantly as a retrograde driving force of evolution. In this context we understand the term retrograde evolution as a degeneration of living systems from higher complexity towards living systems with lower complexity. With the proposed hypothesis we have closed the gap between Darwinism and Lamarckism early in the evolutionary process. By Lamarckism, the action of a special principle called complexification force is understood here rather than inheritance of acquired characteristics.

Genomic Intelligence as Über Bio-Cybersecurity: The Gödel Sentence in Immuno-Cognitive Systems

This paper gives formal foundations and evidence from gene science in the post Barbara McClintock era that the Gödel Sentence, far from being an esoteric construction in mathematical logic, is ubiquitous in genomic intelligence that evolved with multi-cellular life. Conditions uniquely found in the Adaptive Immune System (AIS) and Mirror Neuron System (MNS), termed the genomic immuno-cognitive system, coincide with three building blocks in computation theory of Gödel, Turing and Post (G-T-P). (i) Biotic elements have unique digital identifiers with gene codes executing 3D self-assembly for morphology and regulation of the organism using the recursive operation of Self-Ref (Self-Reference) with the other being a self-referential projection of self. (ii) A parallel offline simulation meta/mirror environment in 1–1 relation to online machine executions of self-codes gives G-T-P Self-Rep (Self-Representation). (iii) This permits a digital biotic entity to self-report that it is under attack by a biotic malware or non-self antigen in the format of the Gödel sentence, resulting in the “smarts” for contextual novelty production. The proposed unitary G-T-P recursive machinery in AIS and in MNS for social cognition yields a new explanation that the Interferon Gamma factor, known for friend-foe identification in AIS, is also integral to social behaviors. New G-T-P bio-informatics of AIS and novel anti-body production is given with interesting testable implications for COVID-19 pathology.

Cell Communications among Microorganisms, Plants, and Animals: Origin, Evolution, and Interplays

Cellular communications play pivotal roles in multi-cellular species, but they do so also in uni-cellular species. Moreover, cells communicate with each other not only within the same individual, but also with cells in other individuals belonging to the same or other species. These communications occur between two unicellular species, two multicellular species, or between unicellular and multicellular species. The molecular mechanisms involved exhibit diversity and specificity, but they share common basic features, which allow common pathways of communication between different species, often phylogenetically very distant. These interactions are possible by the high degree of conservation of the basic molecular mechanisms of interaction of many ligand-receptor pairs in evolutionary remote species. These inter-species cellular communications played crucial roles during Evolution and must have been positively selected, particularly when collectively beneficial in hostile environments. It is likely that communications between cells did not arise after their emergence, but were part of the very nature of the first cells. Synchronization of populations of non-living protocells through chemical communications may have been a mandatory step towards their emergence as populations of living cells and explain the large commonality of cell communication mechanisms among microorganisms, plants, and animals.

One-carbon metabolism, folate, zinc and translation

The translation process, central to life, is tightly connected to the one-carbon (1-C) metabolism via a plethora of macromolecule modifications and specific effectors. Using manual genome annotations and putting together a variety of experimental studies, we explore here the possible reasons of this critical interaction, likely to have originated during the earliest steps of the birth of the first cells. Methionine, S-adenosylmethionine and tetrahydrofolate dominate this interaction. Yet, 1-C metabolism is unlikely to be a simple frozen accident of primaeval conditions. Reactive 1-C species (ROCS) are buffered by the translation machinery in a way tightly associated with the metabolism of iron-sulfur clusters, zinc and potassium availability, possibly coupling carbon metabolism to nitrogen metabolism. In this process, the highly modified position 34 of tRNA molecules plays a critical role. Overall, this metabolic integration may serve both as a protection against the deleterious formation of excess carbon under various growth transitions or environmental unbalanced conditions and as a regulator of zinc homeostasis, while regulating input of prosthetic groups into nascent proteins. This knowledge should be taken into account in metabolic engineering.

Molecular identification of bio-fluids in gas phase using infrared spectroscopy

Bio-fluids are the source of a large number of metabolites. Identification and quantification of them can be an efficient step for understanding the internal chemistry of the body as well as for developing objective diagnostics of diseases. Several techniques have been developed so far; however, their metabolite identification and/or quantification are not reliable enough for acceptance by clinicians. As another promising step in this direction, we push infrared spectroscopy of bio-fluids in gas phase. Here we discuss features of breath and urine headspace realized with Fourier transform infrared spectroscopy. Molecular identification procedures based on component analysis of gas samples are proposed. In this paper, we show that aggregate data from different bio-fluids in gas phase can strengthen the diagnostics of the body state and disease.

Intersecting Xenobiology and Neometabolism To Bring Novel Chemistries to Life

The diversity of life relies on a handful of chemical elements (carbon, oxygen, hydrogen, nitrogen, sulfur and phosphorus) as part of essential building blocks; some other atoms are needed to a lesser extent, but most of the remaining elements are excluded from biology. This circumstance limits the scope of biochemical reactions in extant metabolism - yet it offers a phenomenal playground for synthetic biology. Xenobiology aims to bring novel bricks to life that could be exploited for (xeno)metabolite synthesis. In particular, the assembly of novel pathways engineered to handle nonbiological elements (neometabolism) will broaden chemical space beyond the reach of natural evolution. In this review, xeno-elements that could be blended into nature's biosynthetic portfolio are discussed together with their physicochemical properties and tools and strategies to incorporate them into biochemistry. We argue that current bioproduction methods can be revolutionized by bridging xenobiology and neometabolism for the synthesis of new-to-nature molecules, such as organohalides.

Insights Into the Origin of Life: Did It Begin from HCN and H2O?

The seminal Urey-Miller experiments showed that molecules crucial to life such as HCN could have formed in the reducing atmosphere of the Hadean Earth and then dissolved in the oceans. Subsequent proponents of the "RNA World" hypothesis have shown aqueous HCN to be the starting point for the formation of the precursors of RNA and proteins. However, the conditions of early Earth suggest that aqueous HCN would have had to react under a significant number of constraints. Therefore, given the limiting conditions, could RNA and protein precursors still have formed from aqueous HCN? If so, what mechanistic routes would have been followed? The current computational study, with the aid of the ab initio nanoreactor (AINR), a powerful new tool in computational chemistry, addresses these crucial questions. Gratifyingly, not only do the results from the AINR approach show that aqueous HCN could indeed have been the source of RNA and protein precursors, but they also indicate that just the interaction of HCN with water would have sufficed to begin a series of reactions leading to the precursors. The current work therefore provides important missing links in the story of prebiotic chemistry and charts the road from aqueous HCN to the precursors of RNA and proteins.

Towards a General Definition of Life

A new definition of life is proposed and discussed in the present article. It is formulated by modifying and extending NASA's working definition of life, which postulates that life is a "self-sustaining chemical system capable of Darwinian evolution". The new definition includes a thermodynamical aspect of life as a far from equilibrium system and considers the flow of information from the environment to the living system. In our derivation of the definition of life we have assumed the hypothesis, that during the emergence of life evolution had to first involve autocatalytic systems that only subsequently acquired the capacity of genetic heredity. The new proposed definition of life is independent of the mode of evolution, regardless of whether Lamarckian or Darwinian evolution operated at the origins of life and throughout evolutionary history. The new definition of life presented herein is formulated in a minimal manner and it is general enough that it does not distinguish between individual (metabolic) network and the collective (ecological) one. The newly proposed definition of life may be of interest for astrobiology, research into the origins of life or for efforts to produce synthetic or artificial life, and it furthermore may also have implications in the cognitive and computer sciences.

Systems protobiology: origin of life in lipid catalytic networks

Life is that which replicates and evolves, but there is no consensus on how life emerged. We advocate a systems protobiology view, whereby the first replicators were assemblies of spontaneously accreting, heterogeneous and mostly non-canonical amphiphiles. This view is substantiated by rigorous chemical kinetics simulations of the graded autocatalysis replication domain (GARD) model, based on the notion that the replication or reproduction of compositional information predated that of sequence information. GARD reveals the emergence of privileged non-equilibrium assemblies (composomes), which portray catalysis-based homeostatic (concentration-preserving) growth. Such a process, along with occasional assembly fission, embodies cell-like reproduction. GARD pre-RNA evolution is evidenced in the selection of different composomes within a sparse fitness landscape, in response to environmental chemical changes. These observations refute claims that GARD assemblies (or other mutually catalytic networks in the metabolism first scenario) cannot evolve. Composomes represent both a genotype and a selectable phenotype, anteceding present-day biology in which the two are mostly separated. Detailed GARD analyses show attractor-like transitions from random assemblies to self-organized composomes, with negative entropy change, thus establishing composomes as dissipative systems-hallmarks of life. We show a preliminary new version of our model, metabolic GARD (M-GARD), in which lipid covalent modifications are orchestrated by non-enzymatic lipid catalysts, themselves compositionally reproduced. M-GARD fills the gap of the lack of true metabolism in basic GARD, and is rewardingly supported by a published experimental instance of a lipid-based mutually catalytic network. Anticipating near-future far-reaching progress of molecular dynamics, M-GARD is slated to quantitatively depict elaborate protocells, with orchestrated reproduction of both lipid bilayer and lumenal content. Finally, a GARD analysis in a whole-planet context offers the potential for estimating the probability of life's emergence. The invigorated GARD scrutiny presented in this review enhances the validity of autocatalytic sets as a bona fide early evolution scenario and provides essential infrastructure for a paradigm shift towards a systems protobiology view of life's origin.