[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (43)

Search Parameters:
Keywords = prebiotic information systems

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
17 pages, 2476 KiB  
Study Protocol
Effects of Synbiotic Supplementation on Bone and Metabolic Health in Caucasian Postmenopausal Women: Rationale and Design of the OsteoPreP Trial
by Alisa Turbić, Liesbeth Vandenput, Anoohya Gandham and Mattias Lorentzon
Nutrients 2024, 16(23), 4219; https://doi.org/10.3390/nu16234219 - 6 Dec 2024
Viewed by 800
Abstract
Background/Objectives: Correction of decreased diversity of the gut microbiome, which is characteristic of menopause, by supplementation with a synbiotic may attenuate or prevent dysbiosis processes and preserve bone mass. We describe the rationale and design of the OsteoPreP trial aimed at evaluating the [...] Read more.
Background/Objectives: Correction of decreased diversity of the gut microbiome, which is characteristic of menopause, by supplementation with a synbiotic may attenuate or prevent dysbiosis processes and preserve bone mass. We describe the rationale and design of the OsteoPreP trial aimed at evaluating the effects of 12 months of supplementation with a synbiotic on bone and metabolic health in postmenopausal Caucasian women. Methods: This is a randomized, double-blinded, placebo-controlled trial among 160 Caucasian, postmenopausal women with no current diagnosis of osteoporosis or supplementation with pro- or prebiotics, and no medical treatment affecting bone turnover. Dual-energy X-ray absorptiometry scans will be conducted at screening to confirm absence of osteoporosis. The primary outcome is the relative change (%) in total bone mineral density of the distal tibia at 12 months post-treatment between the active and placebo groups, as determined via high-resolution peripheral quantitative computed tomography. Secondary outcomes are the effects on immune system modulation and cognition, gut microbiota composition, and musculoskeletal and metabolic functions, with particular emphasis on blood glucose regulation. Conclusions: The trial will inform on the efficacy and safety of a synbiotic containing both aerobic and anerobic bacterial strains and a prebiotic fiber on reduction in bone loss and on indices of blood glucose regulation. This trial may pave the way for an exciting field of translational research and be the underpinnings of the prevention strategy of osteoporosis and the management of metabolic dysfunction in postmenopausal women. The trial is registered with clinicaltrials.gov (NCT05348694). Full article
(This article belongs to the Section Nutrition in Women)
Show Figures

Figure 1

Figure 1
<p>Complications of gut microbiota dysbiosis. CKD = chronic kidney disease; IBD = inflammatory bowel disease.</p>
Full article ">Figure 2
<p>Summary of OsteoPreP trial visits and activities. AE = adverse event; Ax = assessment; Hx = history; IP = investigational product; HbA1c = glycated hemoglobin; BP = blood pressure; BMI = body mass index; HWR = height-to-weight ratio; DXA = dual-energy X-ray absorptiometry; HR-pQCT = high-resolution peripheral quantitative computed tomography; CGM = continuous glucose monitor; Glu = glucose; vit B12 = vitamin B12; Treg = T regulatory cell; OGTT = oral glucose tolerance test; ^ = musculoskeletal subgroup.</p>
Full article ">Figure 3
<p>Summary of OsteoPreP trial primary and secondary outcomes. BP = Blood pressure; DASS-21 = Depression Anxiety Stress Scale-21; EQ-5D = EuroQOL Five Dimensions quality of life scale; SIAS = Social Interaction Anxiety Scale; WEMWBS = Warwick-Edinburgh Mental Wellbeing Scale; Pain VAS = Visual Analogue Scale for pain; BMD = Bone Mineral Density; DXA = Dual-energy X-ray Absorptiometry; HR-pQCT = High-Resolution Peripheral Quantitative Computed Tomography; CGM = Continuous Glucose Monitor; OGTT = Oral Glucose Tolerance Test; HbA1c = Hemoglobin A1C blood sugar (glucose) level test; ^ = musculoskeletal subgroup at twelve months only; SCFA = Short Chain Fatty Acids; GLP-1 = Glucagon-Like Peptide-1 receptor agonist; GSRS = Gastrointestinal Symptom Rating Scale; * = musculoskeletal subgroup will undergo OGTT.</p>
Full article ">Figure 4
<p>OsteoPreP trial inclusion and exclusion criteria and screening process. * Do not meet the inclusion criteria; ^ = DXA, BP, or HbA1c ineligible participant; Dx = diagnosis; DXA = dual-energy X-ray absorptiometry; COPD = chronic obstructive pulmonary disease; IBD = inflammatory bowel disease; CD = celiac disease; T1DM = type 1 diabetes mellitus; T2DM = type 2 diabetes mellitus; CLD = chronic liver disease; HbA1c = hemoglobin A1C blood sugar (glucose) level test; BP = blood pressure; HRT = hormone replacement therapy; RANK = receptor activator of NF-κB ligand; SERMs = selective estrogen receptor modulators.</p>
Full article ">
14 pages, 970 KiB  
Article
Optimization of Microbial Glycogen Production by Saccharomyces cerevisiae CEY1
by Hyungseok Choi, In-Seok Yeo, Godfrey Mwiti, Toan Nguyen Song Dinh, Hyein Kang, Chang Sup Kim and Jaehan Kim
Fermentation 2024, 10(8), 388; https://doi.org/10.3390/fermentation10080388 - 29 Jul 2024
Viewed by 1783
Abstract
Glycogen is a highly branched polyglucan utilized as a carbohydrate reserve in major living systems. Industrially, it is used as a prebiotic and in the nanoencapsulation of drugs and nutraceuticals. In this study, optimal fermentation conditions enabling the highest glycogen accumulation in Saccharomyces [...] Read more.
Glycogen is a highly branched polyglucan utilized as a carbohydrate reserve in major living systems. Industrially, it is used as a prebiotic and in the nanoencapsulation of drugs and nutraceuticals. In this study, optimal fermentation conditions enabling the highest glycogen accumulation in Saccharomyces cerevisiae were experimentally evaluated for possible mass production. Production efficiency was assessed by comparing specific growth rates, specific glycogen production rates, and glycogen yields under each condition. The results demonstrated that fermentation at 30 °C with an aeration rate of 3 vvm using a medium containing 120 g/L glucose without ethanol was optimal for robust cell growth and maximum glycogen yield. Additionally, a rich medium outperformed a minimally defined medium, and a single sugar carbon source, as opposed to mixed sugars, resulted in significantly higher cell growth and glycogen yields (p < 0.05). The optimized fermentation parameters enabled a glycogen production rate of up to 0.232 ± 0.012 g-glycogen/g-cell/h and a glycogen yield of 0.603 ± 0.006 g-glycogen/g-glucose. These results provide meaningful information for future studies and/or large-scale glycogen production using S. cerevisiae. Full article
(This article belongs to the Section Industrial Fermentation)
Show Figures

Figure 1

Figure 1
<p>Results of fermentation with varying initial glucose concentrations in <span class="html-italic">Saccharomyces cerevisiae</span> CEY1. (<b>a</b>) Dry cell weight, (<b>b</b>) Specific growth rate, (<b>c</b>) Specific glycogen production rate, and (<b>d</b>) Glycogen yield. The ΔDCW (dry cell wight, g/L) denotes the difference in DCW between the initiation and exhaustion of each carbon source (glucose or ethanol). In panels (<b>a</b>,<b>c</b>), the black bar strictly indicates values in the glucose phase, while the gray bar represents values strictly during the ethanol consumption phase. The error bars indicate the standard deviation.</p>
Full article ">Figure 2
<p>Results of fermentation using mixed sugar (maltose syrup) in <span class="html-italic">S. cerevisiae</span> CEY1. (<b>a</b>) Fermentation profiles and (<b>b</b>) Specific glycogen production rate (g-glycogen/g-cell/h). Two independent fermentation experiments were conducted, and the error was less than 1% RSD (relative standard deviation).</p>
Full article ">Figure 3
<p>Results of the effect of the type of media (<b>i</b>), temperature (<b>ii</b>), and the initial ethanol concentration (<b>iii</b>) on glycogen production in <span class="html-italic">S. cerevisiae</span> CEY1. (<b>a</b>) Specific growth rate, (<b>b</b>) Specific glycogen production rate, and (<b>c</b>) Glycogen yield. The error bars indicate the standard deviation.</p>
Full article ">
13 pages, 1855 KiB  
Review
The Origin of RNA and the Formose–Ribose–RNA Pathway
by Gaspar Banfalvi
Int. J. Mol. Sci. 2024, 25(12), 6727; https://doi.org/10.3390/ijms25126727 - 19 Jun 2024
Viewed by 1377
Abstract
Prebiotic pre-Darwinian reactions continued throughout biochemical or Darwinian evolution. Early chemical processes could have occurred on Earth between 4.5 and 3.6 billion years ago when cellular life was about to come into being. Pre-Darwinian evolution assumes the development of hereditary elements but does [...] Read more.
Prebiotic pre-Darwinian reactions continued throughout biochemical or Darwinian evolution. Early chemical processes could have occurred on Earth between 4.5 and 3.6 billion years ago when cellular life was about to come into being. Pre-Darwinian evolution assumes the development of hereditary elements but does not regard them as self-organizing processes. The presence of biochemical self-organization after the pre-Darwinian evolution did not justify distinguishing between different types of evolution. From the many possible solutions, evolution selected from among those stable reactions that led to catalytic networks, and under gradually changing external conditions produced a reproducible, yet constantly evolving and adaptable, living system. Major abiotic factors included sunlight, precipitation, air, minerals, soil and the Earth’s atmosphere, hydrosphere and lithosphere. Abiotic sources of chemicals contributed to the formation of prebiotic RNA, the development of genetic RNA, the RNA World and the initial life forms on Earth and the transition of genRNA to the DNA Empire, and eventually to the multitude of life forms today. The transition from the RNA World to the DNA Empire generated new processes such as oxygenic photosynthesis and the hierarchical arrangement of processes involved in the transfer of genetic information. The objective of this work is to unite earlier work dealing with the formose, the origin and synthesis of ribose and RNA reactions that were published as a series of independent reactions. These reactions are now regarded as the first metabolic pathway. Full article
(This article belongs to the Section Molecular Genetics and Genomics)
Show Figures

Figure 1

Figure 1
<p>Demonstration of β-D-ribose being the best and lyxose the least fitting pentose in nucleotide building blocks of nucleic acids. (<b>a</b>) In ribonucleotides, the bulky base at C1′ and the phosphate group at C5′ position are distantly located from each other to be outside the van der Waals distance. In ribose, the C2′ and C3′ hydroxyl groups are on the opposite side of the pentose ring. Due to the major C2′-endo and the minor C3′-exo conformation the hydroxyl groups of the C2′ and C3′ point in different directions and are far enough apart to allow free rotation. (<b>b</b>) In arabinonucleotide, the C2′ hydroxyl is above the plane of the ring but the base falls slightly within van der Waals distance relative to the C2′-OH. The spherical hindrance of these groups prevents the free rotation, as is shown by the upper right black arrow in this panel. (<b>c</b>) Xylonucleotide: the steric vicinity of C3′ and C5′ substituents hinders the free rotation shown by the black arrow at the left side of this panel. (<b>d</b>) Lyxonucleotide: all functional groups would be squeezed inside the cage above the plane of the ring. The spatial incompatibility is caused by the lack of space. Free rotation is prevented by the C5′-phosphate, C3′-OH and C2′-OH, as shown by the black arrows [<a href="#B20-ijms-25-06727" class="html-bibr">20</a>]. Reproduced with permission from the free PMC article from Banfalvi, G. “Why ribose was selected as the sugar component of nucleic acids”. <span class="html-italic">DNA Cell Biol.</span>, 2006, Figure 5.</p>
Full article ">Figure 2
<p>Demonstration of anomeric glycosidic bonds of nucleotides impacting the formation of the helical structure. (<b>a</b>) In β-D ribonucleotides, the anomeric bonds are in β position, far enough to secure free rotation. (<b>b</b>) In the α-D-ribonucleotide, the anomeric bond is within Van der Waals distance between the base and sugar preventing free rotation. (<b>c</b>) β-D arabinonucleotide: the 2′deoxy H-bond and base are too close and prevent free rotation indicated with the black arrow. (<b>d</b>) Lack of double-stranded structure formation in DNA containing exclusively α-D-ribose. (<b>e</b>) Base pairing and double DNA structure formed in the presence of ribonucleotides. (<b>f</b>) Lack of base pairing at the lower end of the DNA structure in the presence of ‘one pair of α-D-arabinose. Reproduced with permission from Banfalvi, G. “Prebiotic Pathway from ribose to RNA formation”. <span class="html-italic">Int. J. Mol. Sci.</span>, 2021, Figure 2.</p>
Full article ">Figure 3
<p>The formose–ribose–RNA pathway. The upper box contains the formose reaction starting with (<b>a</b>) two formaldehyde molecules, and (<b>b</b>) condensed to glycolaldehyde. (<b>c</b>) The subsequent aldol reaction extended the chain length by two carbon units, forming glyceraldehyde (<b>d</b>) that underwent an aldose–ketose isomerization. (<b>e</b>) Glyceraldehyde reacts with glycolaldehyde to bring about pentulose. (<b>f</b>) Selective isomerization takes place between pentulose (<b>e</b>) and ribose (<b>f</b>) favoring the nucleophilic addition of D-ribose (<b>g</b>) and its ring formation to D-ribose (<b>h</b>). The nucleophylic reaction produces racemic ribose. Enantioselective β-D-ribose is depicted only to show its further metabolism in the formose–ribose–RNA pathway. Reproduced with permission from Banfalvi, G. “Prebiotic Pathway from ribose to RNA formation”. <span class="html-italic">Int. J. Mol. Sci.</span>, 2021, Figures 1 and 3.</p>
Full article ">Figure 4
<p>DNA Empire: Hierarchical arrangement of processes involved in the transfer of genetic information from DNA through RNA to proteins. Processes belonging to the intracellular transfer of genetic information are DNA ↔ DNA transfer processes. (1) Mutation: DNA =&gt; DNA′. (2) DNA repair: DNA′ =&gt; DNA. (3) Recombination: crossover, gene conversion (DNA′-DNA hybridization). Recombination can take place intracellularly but is mainly an intercellular process. (4) Apoptosis (programmed cell death, high levels of DNA damage). (5). Aging, several mutations, persistent DNA damage: DNA′ =&gt; DNA″ =&gt; DNA‴. (6). Malignant transformation with multiple mutations: DNA =&gt; DNAn′ (persistent DNA damage, ma)ny mutations, mutant p53). (7). DNA replication: DNA—DNA reduplication (high fidelity, HiFi process, 1:10<sup>10</sup> misincorporated deoxyribonucleotide). (8–9). DNA =&gt; RNA transfer. (8). Transcription: DNA =&gt; RNA (medium fidelity, MeFi process, 1:10<sup>5</sup> misincorporated ribonucleotide). (9). Reverse transcription: RNA =&gt; DNA (in retroviruses). (10). RNA replication: RNA =&gt; RNA (in RNA viruses). (11). Processes belonging to posttranscriptional modifications: 5′-cap formation, 3′-polyA formation, splicing. (12). Translation: RNA =&gt; protein (low fidelity, LoFi process, 1:10<sup>4</sup> misincorporated amino acid) (13). Processes belonging to protein modification: protein splicing, transglutamination. (14). Protein targeting: information reaches intra- or extracellular destination [<a href="#B49-ijms-25-06727" class="html-bibr">49</a>]. Reproduced with permission from Banfalvi, G. “Prebiotic Pathway from ribose to RNA formation”. <span class="html-italic">Int. J. Mol. Sci.</span>, 2021, Figure 5.</p>
Full article ">
13 pages, 928 KiB  
Review
On the Re-Creation of Protoribosome Analogues in the Lab
by Ilana Agmon
Int. J. Mol. Sci. 2024, 25(9), 4960; https://doi.org/10.3390/ijms25094960 - 2 May 2024
Cited by 1 | Viewed by 1032
Abstract
The evolution of the translation system is a fundamental issue in the quest for the origin of life. A feasible evolutionary scenario necessitates the autonomous emergence of a protoribosome capable of catalyzing the synthesis of the initial peptides. The peptidyl transferase center (PTC) [...] Read more.
The evolution of the translation system is a fundamental issue in the quest for the origin of life. A feasible evolutionary scenario necessitates the autonomous emergence of a protoribosome capable of catalyzing the synthesis of the initial peptides. The peptidyl transferase center (PTC) region in the modern ribosomal large subunit is believed to retain a vestige of such a prebiotic non-coded protoribosome, which would have self-assembled from random RNA chains, catalyzed peptide bond formation between arbitrary amino acids, and produced short peptides. Recently, three research groups experimentally demonstrated that several distinct dimeric constructs of protoribosome analogues, derived predicated on the approximate 2-fold rotational symmetry inherent in the PTC region, possess the ability to spontaneously fold, dimerize, and catalyze the formation of peptide bonds and of short peptides. These dimers are examined, aiming at retrieving information concerned with the characteristics of a prebiotic protoribosome. The analysis suggests preconditions for the laboratory re-creation of credible protoribosome analogues, including the preference of a heterodimer protoribosome, contradicting the common belief in the precedence of homodimers. Additionally, it derives a dynamic process which possibly played a role in the spontaneous production of the first bio-catalyzed peptides in the prebiotic world. Full article
(This article belongs to the Section Biochemistry)
Show Figures

Figure 1

Figure 1
<p>The protoribosome. (<b>a</b>) The symmetrical region encircling the PTC within the rRNA of the large subunit (pdb 2aw4). H68H71 were removed to divulge the PTC. A-region in blue hues, P-region in green hues, throughout. (<b>b</b>) A 2D scheme of the PTC neighborhood in symmetrical representation. The part assigned to the dimeric protoribosome (DPR) is framed and presented with stronger hues. The A- and P-loops, which make part of the SymR, are depicted by lighter background and the non-symmetrical extensions, H75 and H91, are indicated by blue and green letters and dots. Nucleotides conserved by more than 97% in each of the three domains of life are indicated by capital letters. (<b>c</b>) Overlap of the DPR pocket as found in archaea (pdb 1vq6), bacteria (pdb 2wdl), and eukarya (pdb 3u5d), portraying the extreme tertiary conservation of this region. The pocket is projected approximately along the symmetry axis and peptide bond is formed at the bottom of the cavity.</p>
Full article ">Figure 2
<p>Fold and secondary structure of models I, II, and III, assumed to emulate the prebiotic protoribosome. (<b>a</b>) Structure of the P<sub>I</sub>P<sub>I</sub>’ and P<sub>III</sub>P<sub>III</sub>’ homodimers from models I and III (pdb 1vy4). GNRA loop derived from H93 is appended onto the truncated H89. A perpendicular view showing differences in the folds of P<sub>I</sub> and P<sub>III</sub> monomers is given in <a href="#app1-ijms-25-04960" class="html-app">Figure S1</a>. (<b>b</b>) A 2D scheme of the P<sub>I</sub>, P<sub>III</sub> monomer sequences from Thermus thermophilus. Nucleotides common to both models are drawn in black and those existing solely in the P<sub>III</sub>-tailed monomer are shown in orange. The loops artificially capping the truncated helices are drawn in red. (<b>c</b>) Structure of the extended A<sub>II</sub>P<sub>II</sub> heterodimer from model II. The DPR parts composing the PTC pocket are drawn in darker hues, the A-, P-loops in lighter hues, and the non-symmetrical extensions of H75 and H91 in lime and silver, respectively. (<b>d</b>) A 2D scheme of the P<sub>II</sub> monomer sequence from Thermus thermophilus.</p>
Full article ">Figure 3
<p>SecM peptide (magenta) in the PTC (pdb 3jbv). The nine C-terminal residues (Gln21-Pro29) interact with the PTC nucleotides included in model II.</p>
Full article ">
32 pages, 10632 KiB  
Review
Proto-Neurons from Abiotic Polypeptides
by Panagiotis Mougkogiannis and Andrew Adamatzky
Encyclopedia 2024, 4(1), 512-543; https://doi.org/10.3390/encyclopedia4010034 - 8 Mar 2024
Cited by 2 | Viewed by 1919
Abstract
To understand the origins of life, we must first gain a grasp of the unresolved emergence of the first informational polymers and cell-like assemblies that developed into living systems. Heating amino acid mixtures to their boiling point produces thermal proteins that self-assemble into [...] Read more.
To understand the origins of life, we must first gain a grasp of the unresolved emergence of the first informational polymers and cell-like assemblies that developed into living systems. Heating amino acid mixtures to their boiling point produces thermal proteins that self-assemble into membrane-bound protocells, offering a compelling abiogenic route for forming polypeptides. Recent research has revealed the presence of electrical excitability and signal processing capacities in proteinoids, indicating the possibility of primitive cognitive functions and problem-solving capabilities. This review examines the characteristics exhibited by proteinoids, including electrical activity and self-assembly properties, exploring the possible roles of such polypeptides under prebiotic conditions in the emergence of early biomolecular complexity. Experiments showcasing the possibility of unconventional computing with proteinoids as well as modelling proteinoid assemblies into synthetic proto-brains are given. Proteinoids’ robust abiogenic production, biomimetic features, and computational capability shed light on potential phases in the evolution of polypeptides and primitive life from the primordial environment. Full article
(This article belongs to the Section Biology & Life Sciences)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Molecular model of an 11-residue thermal proteinoids peptide chain containing alternating glutamic acid and arginine units. Each glutamic acid (L-Glu) aspartic acid (L-Asp) and phenylalanine (L-Phe) monomer is depicted in ball-and-stick representation with nitrogen atoms colored blue, oxygen red, carbon dark grey, and hydrogen light grey. The polypeptide backbone illustrates structure formed through thermal condensation polymerisation which can further self-assemble into higher-order proteinoid microspheres. The proteinoid structure was generated using ChimeraX molecular visualisation software.</p>
Full article ">Figure 2
<p>The mechanism underlying the aggregation of proteinoids. Proteinoids have the inherent ability to undergo self-assembly and disintegration processes, resulting in the formation of complex molecular structures like microspheres. This process is facilitated through the presence of hydrophobic interactions and hydrogen bonding between proteinoid branches, which bears a resemblance to the biological processes of protein folding and aggregation. Proteinoid aggregates exhibit a perpetual influx and efflux of material, hence sustaining an internal state characterised by constant change. Various environmental conditions, including temperature, pH, and ionic strength, have the ability to influence the equilibrium towards specific aggregated states [<a href="#B126-encyclopedia-04-00034" class="html-bibr">126</a>].</p>
Full article ">Figure 3
<p>(<b>a</b>,<b>b</b>) Perfect proteinoid microspheres self-assembled from a supersaturated precursor solution. Microspheres have a diameter of 1.2 microns. Magnification 60,000×, scale bar 500 nm. (<b>c</b>) Cubic crystal with a central cavity formed after applying an electrical voltage to proteinoids. The cubic morphology suggests reorganisation of proteinoids under electrical stimuli. Magnification 8000×, scale bar 5 μm. (<b>d</b>) Nanoscale proteinoid spheres arranged on the surface of a cubic crystal substrate. This highlights preferential interactions between proteinoids and crystal surfaces. Magnification 40,000×, scale bar 1 μm.</p>
Full article ">Figure 4
<p>Memfractance current–voltage characteristics of L-Glu:L-Arg proteinoids. The I–V curve shows the nonlinear memfractance behavior, with currents of −3.95 μA at −1 V and 3.57 μA at +1 V. Hysteresis is observed around 0 V, with currents of −0.6898 μA when sweeping from high to low voltages and 0.9043 μA when sweeping low to high. The asymmetric I-V response demonstrates that proteinoids can exhibit memristive-like electrical properties that may be harnessed for bioelectronic applications. Further tuning the composition and assembly conditions enables engineering proteinoids as adaptive, multifunctional electronic materials.</p>
Full article ">Figure 5
<p>Memfractance of L-Glu:L-Arg proteinoid gel in hydroxyapatite (HAP). The I–V characteristics were measured with the proteinoid gel immersed in 200 mL HAP solution at pH 7.4, 0.15 M ionic strength, and 37 °C. The HAP environment enhances memfractance, with currents of −77.4 μA at −1 V and 79.788 μA at +1 V. The near-zero current of −0.494 μA at 0 V indicates reduced hysteresis. Incorporating biomimetic minerals thus tunes proteinoids’ memfractance performance.</p>
Full article ">Figure 6
<p>Amino acids undergo thermal polymerisation resulting in proteinoids. By means of intramolecular cyclisation and condensation reactions, heating glutamic acid, aspartic acid, and lysine produces pyroglutamic acid, cyclic diaspartic acid, and caprolactam, respectively (<b>top</b>). Cyclic amino acid derivatives have the ability to undergo additional polymerisation, resulting in the formation of proteinoid microsphere chains (see (<b>bottom</b>)). The figure depicts the standard chemical reactions that occur during the synthesis of proteinoids from amino acid precursors. By manipulating the monomer composition and heating conditions, it is possible to produce proteinoids with specific properties under control [<a href="#B187-encyclopedia-04-00034" class="html-bibr">187</a>].</p>
Full article ">Figure 7
<p>Long-term electrical activity in proteinoids microspheres. Voltage recording over 21 h exhibits characteristic spiking patterns, with magnified inserts showing details of spikes over time. The continued signaling demonstrates sustained excitability arising from the proteinoids’ self-assembly.</p>
Full article ">Figure 8
<p>A typical spike in proteinoids electrical potential. The spike displays rapid depolarisation and repolarisation phases. This transient electrical event results from electrostatic interactions between proteinoid dipoles, which produce propagation of excitation through the microsphere network. The spike shape shows proteinoids can mimic key features of neural action potentials.</p>
Full article ">Figure 9
<p>Onion-like proteinoid–CAP nanostructures. This is a scanning electron micrograph that displays proteinoids arranged in many layers around a carbonate apatite (CAP) core. The proteinoids are templated on HAP substrates. The onion-like structure is formed due to the selective aggregation of proteinoids around the mineral particles during nucleation. The scale bar is 500 nanometers. The magnification is 60,000 times. The spot size is 2.0 and the accelerating voltage is 2.0 kilovolts.</p>
Full article ">Figure 10
<p>The PSI and PPI values of several proteinoids are shown in the colour map. The postsynaptic index, or PSI, measures the strength of interneuronal connections in a network, either chemically or functionally. For post-postsynaptic index, see PPI. It measures how effective interneuronal connections are within a certain network. Lighter shades of yellow imply higher PPI values, while darker shades of blue suggest higher PSI values. The relationship between postsynaptic and presynaptic neurons and how they affect proteinoid function is depicted in the map.</p>
Full article ">
22 pages, 1468 KiB  
Article
Origins of Genetic Coding: Self-Guided Molecular Self-Organisation
by Peter R. Wills
Entropy 2023, 25(9), 1281; https://doi.org/10.3390/e25091281 - 31 Aug 2023
Cited by 4 | Viewed by 2585
Abstract
The origin of genetic coding is characterised as an event of cosmic significance in which quantum mechanical causation was transcended by constructive computation. Computational causation entered the physico-chemical processes of the pre-biotic world by the incidental satisfaction of a condition of reflexivity between [...] Read more.
The origin of genetic coding is characterised as an event of cosmic significance in which quantum mechanical causation was transcended by constructive computation. Computational causation entered the physico-chemical processes of the pre-biotic world by the incidental satisfaction of a condition of reflexivity between polymer sequence information and system elements able to facilitate their own production through translation of that information. This event, which has previously been modelled in the dynamics of Gene–Replication–Translation systems, is properly described as a process of self-guided self-organisation. The spontaneous emergence of a primordial genetic code between two-letter alphabets of nucleotide triplets and amino acids is easily possible, starting with random peptide synthesis that is RNA-sequence-dependent. The evident self-organising mechanism is the simultaneous quasi-species bifurcation of the populations of information-carrying genes and enzymes with aminoacyl-tRNA synthetase-like activities. This mechanism allowed the code to evolve very rapidly to the ~20 amino acid limit apparent for the reflexive differentiation of amino acid properties using protein catalysts. The self-organisation of semantics in this domain of physical chemistry conferred on emergent molecular biology exquisite computational control over the nanoscopic events needed for its self-construction. Full article
(This article belongs to the Special Issue Recent Advances in Guided Self-Organization)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Ribosomal protein synthesis. This representation of protein synthesis emphasises the computational role of aminoacyl-tRNA synthetase (aaRS) enzymes in translation. These enzymes (irregular shape, RHS of figure) enforce the rules of the code by specifically matching their amino acid substrate to cognate tRNA molecules, i.e., those bearing nucleotide triplet anticodons (drawn as “legs”) consistent with the codon-to-amino acid rules of the genetic code, shown here as colour-matching. A 4-letter code (different colours for different letter symbols) is depicted. The amino acid added to the growing peptide is a correct (colour-matched) translation of the codon occurring at that point in the genetic message (mRNA).</p>
Full article ">Figure 2
<p>Replication as autocatalysis. The net reaction, components → X, is catalysed by X, a role shown by (<b>a</b>) adding it to both the reactant and product side of the chemical equation; (<b>b</b>) placing it above the arrow; or (<b>c</b>) indicating catalytic feedback (red arrow).</p>
Full article ">Figure 3
<p>Reflexive production of assignment catalysts. (<b>a</b>) Two fictional aaRS-encoding genes, AABABAABABBA and BAABABBABABB, are shown as anti-parallel complementary strands of a single base-paired nucleic acid molecule in accordance with the hypothesis of Rodin &amp; Ohno [<a href="#B37-entropy-25-01281" class="html-bibr">37</a>,<a href="#B38-entropy-25-01281" class="html-bibr">38</a>]. The genes are translated by operation of the coding assignments A→a and B→b between the binary codon alphabet {A, B} and the binary amino acid alphabet {a, b} to produce molecules with amino acid sequences aababaababba and baababbababb representative of two separate classes of aaRS species. (<b>b</b>) The antiparallel genes are depicted in a double helical configuration, and the conserved core structures of the ancestral Class I and II aaRS enzymes are shown.</p>
Full article ">Figure 4
<p>Gene–Replication–Translation (GRT) system. The genetic information in the nucleic acid <b>G</b> encodes a set of aaRS-type assignment catalysts <b>T</b> = {<span class="html-italic">T</span><sub>1</sub>, <span class="html-italic">T</span><sub>2</sub> … <span class="html-italic">T</span><sub>λ</sub>} and a nucleic acid replicase <b>R</b>. The set <b>T</b> catalyses the codon-to-amino acid assignments for the system’s genetic code.</p>
Full article ">Figure 5
<p>Quasi-species bifurcation. Proteins with aaRS-like activity, including weak and non-specific activity, are found throughout a large region of <span class="html-italic">n</span>-dimensional protein sequence space. However, significant activities for the two specific assignments, <span class="html-italic">A</span>→<span class="html-italic">a</span> and <span class="html-italic">B</span>→<span class="html-italic">b</span>, are found within much narrower, separate domains, which are also separate from the domains of the code conflicting <span class="html-italic">A</span>→<span class="html-italic">b</span> and <span class="html-italic">B</span>→<span class="html-italic">a</span> assignments (not shown). Binary coding arises from a symmetry-breaking transition whereby the protein population with aaRS-like activity becomes concentrated from the large, broad domain into the two smaller regions of sequence space.</p>
Full article ">
6 pages, 816 KiB  
Proceeding Paper
How Much Rationality Is Needed for Decision Making?
by Annette Grathoff
Comput. Sci. Math. Forum 2023, 8(1), 12; https://doi.org/10.3390/cmsf2023008012 - 10 Aug 2023
Viewed by 692
Abstract
The Braess paradox (discovered in 1968 by the German mathematician Dietrich Braess) describes how a possible relief of a system, by introducing new possibilities to distribute load or local density in flows inside the system, can actually increase stress on the system. It [...] Read more.
The Braess paradox (discovered in 1968 by the German mathematician Dietrich Braess) describes how a possible relief of a system, by introducing new possibilities to distribute load or local density in flows inside the system, can actually increase stress on the system. It is most often researched in a world of rational decision-makers, who are assumed to cause the worsening situation due to rational optimization of individual interests. In strongly complex networks, the exploitation of new possibilities most probably needs rational decision-makers who can see the use of new possibilities for them. Interestingly, a mechanical analogy of the situation also exists, where new possibilities—in this case for forces in a system to attack—lead to a loss of stability inside the system. In this example, a string that was introduced to relieve the load on two springs leads to counter-intuitive overloading. With the perspective that the evolution of information processing systems is already beginning in a physical and chemical pre-biotic world, this is an interesting case that might give further insight into how and when choices between many possibilities could threaten the function of a system rather than making it more durable and adaptable. The example is discussed based on a review of literature from the humanities as well as the natural sciences. Full article
(This article belongs to the Proceedings of 2023 International Summit on the Study of Information)
Show Figures

Figure 1

Figure 1
<p>Wheatstone electrical network (<b>a</b>) Braess’s street network (<b>b</b>). (<b>a</b>) Wheatstone electrical network with bridging between nodes C and B [<a href="#B8-csmf-08-00012" class="html-bibr">8</a>]. The Wheatstone network is used to measure unknown resistances (Rx). Therefore, one of the three known resistances (R1, R2, and R3) is made adjustable (R2), and its resistance value is adjusted while watching the flow of current over the bridge on a galvanometer. As soon as the measured current is zero, the bridge is effectively removed from the circuit. The circuit becomes a regular series-parallel circuit, where the unknown resistance can be calculated according to the ratios of resistances on the two parallel paths: R2/R1 = Rx/R3. When this condition is satisfied, then the Wheatstone network is said to be balanced. For the identification of the unknown resistance, the unbalanced state of the bridged network is exploited and transferred to the balanced state, where no current flows along the bridge. The unbalanced state is analogous to the road network in (<b>b</b>) with the connecting path. Picture of car: modified from [<a href="#B9-csmf-08-00012" class="html-bibr">9</a>].</p>
Full article ">Figure 2
<p>Braess’s paradox in two Wheatstone network topologies. Left side of (<b>a</b>,<b>b</b>): system with connection. Right side: system with removed connections between paths. (<b>a</b>) Springs: Two springs carrying the mass m in series are coupled via a short inelastic string (bold black); two inelastic strings of equal length connect each spring to the ceiling, respectively the mass m, but are limp (left side of (<b>a</b>)). When the connecting string between springs is cut, counterintuitively, the springs contract, which—with the right length of strings chosen—leads to a lifting of the mass m compared with the previous setting. (right side of (<b>a</b>)). (<b>b</b>) Traffic network: Similar to the case with the springs, narrow, congested parts of roads (analogous to the springs) are connected with each other in series and counteract the equal distribution of traffic volume between two arcs, increasing travel times for the whole system compared with the system where the connecting path has been removed. Picture of car: modified from [<a href="#B9-csmf-08-00012" class="html-bibr">9</a>].</p>
Full article ">
11 pages, 4665 KiB  
Article
Insights into Early Steps of Decanoic Acid Self-Assemblies under Prebiotic Temperatures Using Molecular Dynamics Simulations
by Romina V. Sepulveda, Christopher Sbarbaro, Ma Cecilia Opazo, Yorley Duarte, Fernando González-Nilo and Daniel Aguayo
Membranes 2023, 13(5), 469; https://doi.org/10.3390/membranes13050469 - 28 Apr 2023
Cited by 1 | Viewed by 1884
Abstract
The origin of life possibly required processes in confined systems that facilitated simple chemical reactions and other more complex reactions impossible to achieve under the condition of infinite dilution. In this context, the self-assembly of micelles or vesicles derived from prebiotic amphiphilic molecules [...] Read more.
The origin of life possibly required processes in confined systems that facilitated simple chemical reactions and other more complex reactions impossible to achieve under the condition of infinite dilution. In this context, the self-assembly of micelles or vesicles derived from prebiotic amphiphilic molecules is a cornerstone in the chemical evolution pathway. A prime example of these building blocks is decanoic acid, a short-chain fatty acid capable of self-assembling under ambient conditions. This study explored a simplified system made of decanoic acids under temperatures ranging from 0 °C to 110 °C to replicate prebiotic conditions. The study revealed the first point of aggregation of decanoic acid into vesicles and examined the insertion of a prebiotic-like peptide in a primitive bilayer. The information gathered from this research provides critical insights into molecule interactions with primitive membranes, allowing us to understand the first nanometric compartments needed to trigger further reactions that were essential for the origin of life. Full article
(This article belongs to the Special Issue Membrane Interaction between Lipids, Proteins and Peptides)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Experimental and theorical comparison of decanoic acid vesicles. (<b>A</b>) TEM image of decanoic acid vesicle prepared in ultra-pure water. (<b>B</b>,<b>C</b>) Snapshots after 300 ns of a decanoic acid molecular system at 25 °C.</p>
Full article ">Figure 2
<p>Description of molecular dynamics trajectories of decanoic acid systems under different temperatures. Decanoic acid molecules promote aggregation to form vesicles at different temperatures. Each picture exhibits a representative frame from the DA trajectory. Water molecules are depicted in red.</p>
Full article ">Figure 3
<p>Description of molecular systems from decanoic acid molecules and peptide. The decanoic acid molecules are represented in blue, water as red spheres, and peptide prototype in yellow.</p>
Full article ">Figure 4
<p>Definition of tilt angle between the peptide axis and the bilayer norm vector. (<b>A</b>) The peptide vector was defined by the bond angle between the alpha carbons of residues 1 (blue) and 16 (green). To avoid curvature perturbations, the decanoic acids located near 8 Å from the peptide were the reference to the bilayer norm vector. This approach allowed the evaluation of the angle between the peptide and bilayer, where angles close to 0° represent the insertion of the peptide from the C-terminus to N-terminus (<b>A</b>), angle values close to 90° exhibit a peptide over the bilayer surface (<b>B</b>), and angles close to 180° are related to the peptide insertion from the N-terminus to C-terminus. (<b>C</b>). The tilt angle of the peptide-membrane exhibits a dynamic range between 0° and 180° across the trajectories (<b>D</b>).</p>
Full article ">
21 pages, 747 KiB  
Review
Antioxidant and Anti-Inflammatory Properties of Walnut Constituents: Focus on Personalized Cancer Prevention and the Microbiome
by Nuoxi Fan, Jennifer L. Fusco and Daniel W. Rosenberg
Antioxidants 2023, 12(5), 982; https://doi.org/10.3390/antiox12050982 - 22 Apr 2023
Cited by 12 | Viewed by 10645
Abstract
Walnuts have been lauded as a ‘superfood’, containing a remarkable array of natural constituents that may have additive and/or synergistic properties that contribute to reduced cancer risk. Walnuts are a rich source of polyunsaturated fatty acids (PUFAs: alpha-linolenic acid, ALA), tocopherols, antioxidant polyphenols [...] Read more.
Walnuts have been lauded as a ‘superfood’, containing a remarkable array of natural constituents that may have additive and/or synergistic properties that contribute to reduced cancer risk. Walnuts are a rich source of polyunsaturated fatty acids (PUFAs: alpha-linolenic acid, ALA), tocopherols, antioxidant polyphenols (including ellagitannins), and prebiotics, including fiber (2 g/oz). There is a growing body of evidence that walnuts may contribute in a positive way to the gut microbiome, having a prebiotic potential that promotes the growth of beneficial bacteria. Studies supporting this microbiome-modifying potential include both preclinical cancer models as well as several promising human clinical trials. Mediated both directly and indirectly via its actions on the microbiome, many of the beneficial properties of walnuts are related to a range of anti-inflammatory properties, including powerful effects on the immune system. Among the most potent constituents of walnuts are the ellagitannins, primarily pedunculagin. After ingestion, the ellagitannins are hydrolyzed at low pH to release ellagic acid (EA), a non-flavonoid polyphenolic that is subsequently metabolized by the microbiota to the bioactive urolithins (hydroxydibenzo[b,d]pyran-6-ones). Several urolithins, including urolithin A, reportedly have potent anti-inflammatory properties. These properties of walnuts provide the rationale for including this tree nut as part of a healthy diet for reducing overall disease risk, including colorectal cancer. This review considers the latest information regarding the potential anti-cancer and antioxidant properties of walnuts and how they may be incorporated into the diet to provide additional health benefits. Full article
Show Figures

Figure 1

Figure 1
<p>Walnuts are a rich source of antioxidant polyphenols (ellagitannins (ETs), fiber, and polyunsaturated fatty acids (PUFAs)) as well as being gluten-free in their natural state. Upon ingestion, ETs are metabolized to ellagic acid (EA) under low pH, and then further metabolized to urolithins by the intestinal microbiota within the lumen. Walnuts contain PUFAs, particularly alpha-linolenic acid (ALA), which is metabolized to eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in the liver. Fiber increases microbiota diversity which contributes to the conversion of fiber into short chain fatty acids (SCFAs). Walnuts are also naturally gluten-free and once absorbed, ETs, ALA and SCFAs increase anti-inflammatory activity within the intestinal lining that may help counter diseases such as ulcerative colitis, Crohn’s colitis, and celiac disease as well as having possible anti-cancerous effects. Created with BioRender.com. Compound structures are from PubChem.</p>
Full article ">
16 pages, 2957 KiB  
Review
The Origin of Translation: Bridging the Nucleotides and Peptides
by Xuyuan Guo and Meng Su
Int. J. Mol. Sci. 2023, 24(1), 197; https://doi.org/10.3390/ijms24010197 - 22 Dec 2022
Cited by 3 | Viewed by 3265
Abstract
Extant biology uses RNA to record genetic information and proteins to execute biochemical functions. Nucleotides are translated into amino acids via transfer RNA in the central dogma. tRNA is essential in translation as it connects the codon and the cognate amino acid. To [...] Read more.
Extant biology uses RNA to record genetic information and proteins to execute biochemical functions. Nucleotides are translated into amino acids via transfer RNA in the central dogma. tRNA is essential in translation as it connects the codon and the cognate amino acid. To reveal how the translation emerged in the prebiotic context, we start with the structure and dissection of tRNA, followed by the theory and hypothesis of tRNA and amino acid recognition. Last, we review how amino acids assemble on the tRNA and further form peptides. Understanding the origin of life will also promote our knowledge of artificial living systems. Full article
(This article belongs to the Special Issue Molecular Biology of RNA: Recent Progress)
Show Figures

Figure 1

Figure 1
<p><b>Structure and dissections of a type I tRNA from <span class="html-italic">E. coli</span>.</b> (<b>a</b>) Typical cloverleaf 2D and ribbon diagram 3D structure, different loops and stem are colored accordingly; (<b>b</b>) dissection at anticodon loop; (<b>c</b>) dissection at the acceptor domain and anticodon domain.</p>
Full article ">Figure 2
<p><b>Early models of tRNA evolution.</b> (<b>a</b>) Hopfield model showing trinucleotide interaction with the amino acid at 5′-terminus; (<b>b</b>) Winkler-Oswatitsch and Eigen model showing how tRNA came from RNY-type triplets; (<b>c</b>) Bloch model showing how tRNA came from RNA self-priming and self-templating; (<b>d</b>) Moller model showing how tRNA came from strand replication and ligation.</p>
Full article ">Figure 2 Cont.
<p><b>Early models of tRNA evolution.</b> (<b>a</b>) Hopfield model showing trinucleotide interaction with the amino acid at 5′-terminus; (<b>b</b>) Winkler-Oswatitsch and Eigen model showing how tRNA came from RNY-type triplets; (<b>c</b>) Bloch model showing how tRNA came from RNA self-priming and self-templating; (<b>d</b>) Moller model showing how tRNA came from strand replication and ligation.</p>
Full article ">Figure 3
<p><b>Recent models of tRNA evolution.</b> (<b>a</b>) Di Giulio model showing how tRNA came from homodimer hairpins; (<b>b</b>) Tanaka model showing how tRNA came from two distinct hairpins with bulges; (<b>c</b>) Fox model showing how tRNA came from two identical hairpins with bulges by self-ligation. ANT, anticodon. D, base-determinator.</p>
Full article ">Figure 3 Cont.
<p><b>Recent models of tRNA evolution.</b> (<b>a</b>) Di Giulio model showing how tRNA came from homodimer hairpins; (<b>b</b>) Tanaka model showing how tRNA came from two distinct hairpins with bulges; (<b>c</b>) Fox model showing how tRNA came from two identical hairpins with bulges by self-ligation. ANT, anticodon. D, base-determinator.</p>
Full article ">Figure 4
<p><b>Aminoacyl esterification on the RNA 3′ terminus.</b> (<b>a</b>) Amino acid activation using aaRS in the extant biology; (<b>b</b>) aminoacyl transfer in a nicked duplex; (<b>c</b>) aminoacyl transfer in a nicked loop; (<b>d</b>) phosphoramidate-mediated esterification in a nicked duplex; (<b>e</b>) phosphoramidate-mediated esterification in a nicked loop. Im, imidazole. EDC, 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide. R, amino acid residue.</p>
Full article ">Figure 5
<p><b>Sequence-dependent amino acid coupling.</b> Schematic representation of (<b>a</b>) ‘mononucleotide translation’ in a nicked duplex using one ssRNA as a template and two complementary RNAs with aminoacyl ester and phosphoramidate; (<b>b</b>) peptide formation in a tRNA anticodon loop mimic using m<sup>6</sup>aa<sup>6</sup>A and mnm<sup>5</sup>U. Rectangle and oval shapes represent amino acids. Bold lines represent RNA strands.</p>
Full article ">Figure 6
<p><b>Extant peptidyl transfer.</b> (<b>a</b>) Mechanism of peptidyl transfer and peptide elongation in the extant biology; (<b>b</b>) detailed structure of PTC in the LSU interacts with tRNA (adapted from [<a href="#B110-ijms-24-00197" class="html-bibr">110</a>]). G2252, U2506, and U2585 interact with the CCA end of the tRNA. The enlargement shows the P binding site. UGGU in red indicates an engineered handle for the CCA end in the tRNA.</p>
Full article ">
9 pages, 1615 KiB  
Communication
The Proliferation Inhibitory Effect of Postbiotics Prepared from  Probiotics with Antioxidant Activity against HT-29 Cells
by Yeeun Kim, Hak Jun Kim and Keunho Ji
Appl. Sci. 2022, 12(24), 12519; https://doi.org/10.3390/app122412519 - 7 Dec 2022
Cited by 6 | Viewed by 1792
Abstract
Prebiotics and probiotics have gained much attention in the pursuit of a healthy life. Recently, postbiotics have been spotlighted as next-generation compounds that can improve health. Postbiotics are designated into non-viable, inactivated, and ghost probiotics, and linked to several health benefits for the [...] Read more.
Prebiotics and probiotics have gained much attention in the pursuit of a healthy life. Recently, postbiotics have been spotlighted as next-generation compounds that can improve health. Postbiotics are designated into non-viable, inactivated, and ghost probiotics, and linked to several health benefits for the gut, immune system, and various other aspects of health. This study investigated the anti-proliferation effects of postbiotics against HT-29 cells, a colon cancer cell line. The postbiotics were produced by the ultrasonication method from two Lactobacillus strains (Lactobacillus sp. La1, and La2) and designated to Pobt-La1 and Pobt-La2, respectively, and non-viability was confirmed on the plate media. The anti-proliferation effect was concentration-dependent. The HT-29 cells showed viabilities of 39% and 49% when treated with 300 µL/mL of Pobt-La1 and Pobt-La2, respectively. During observation of the morphological changes of HT-29 cells when treated with IC50, a cell nucleus was not observed but cell condensation was observed. Moreover, in comparison with the control group, a reduced number of cells were observed. Based on these results, it considered that the postbiotic compounds from Lactobacillus La1 and La2 could provide crucial information in the development of anticancer research. Through further research, it would be beneficial to investigate the possibility of using these postbiotics (Pobt-La1 and -La2) as an anticancer drug. Full article
(This article belongs to the Section Applied Microbiology)
Show Figures

Figure 1

Figure 1
<p>WST-1 assay for cell viability. (<b>a</b>) CCD-18Co and (<b>b</b>) HT-29 cells.</p>
Full article ">Figure 2
<p>Morphological change of HT-29 cells by postbiotics.</p>
Full article ">Figure 3
<p>RT-PCR results of apoptosis associated gene expression.</p>
Full article ">Figure 4
<p>Expression of caspase-3 and 8 proteins in HT-29 cells treated with Pobt-La.</p>
Full article ">
17 pages, 520 KiB  
Review
A Sweeter Pill to Swallow: A Review of Honey Bees and Honey as a Source of Probiotic and Prebiotic Products
by Suraiami Mustar and Nurliayana Ibrahim
Foods 2022, 11(14), 2102; https://doi.org/10.3390/foods11142102 - 15 Jul 2022
Cited by 19 | Viewed by 7018
Abstract
Honey bees and honey, have been the subject of study for decades due to their importance in improving health. At times, some of the probiotics may be transferred to the honey stored in the honeycomb. Consumers may benefit from consuming live-probiotics honey, which [...] Read more.
Honey bees and honey, have been the subject of study for decades due to their importance in improving health. At times, some of the probiotics may be transferred to the honey stored in the honeycomb. Consumers may benefit from consuming live-probiotics honey, which can aid in suppressing the reproduction of pathogens in their digestive system. Prebiotics, on the other hand, are mainly carbohydrates that promote the growth of native microflora probiotics in the digestive tract to maintain a healthy environment and improve the gut performance of the host. Therefore, this narrative review aims to present and analyze ten years’ worth of information on the probiotic and prebiotic potential of honey bees and honey since not many review articles were found discussing this topic. Results showed that not many studies have been performed on the probiotic and prebiotic aspects of honey bees and honey. If further research is conducted, isolated probiotics from the bee’s gut combined with honey’s prebiotic properties can be manipulated as potential sources of probiotics and prebiotics for human and animal benefits since they appear to be interrelated and function in symbiosis. Full article
(This article belongs to the Section Nutraceuticals, Functional Foods, and Novel Foods)
Show Figures

Figure 1

Figure 1
<p>Flowchart of the honey bees and honey as a source of probiotics and prebiotics.</p>
Full article ">
54 pages, 11826 KiB  
Review
The Coevolution of Biomolecules and Prebiotic Information Systems in the Origin of Life: A Visualization Model for Assembling the First Gene
by Sankar Chatterjee and Surya Yadav
Life 2022, 12(6), 834; https://doi.org/10.3390/life12060834 - 2 Jun 2022
Cited by 5 | Viewed by 5774
Abstract
Prebiotic information systems exist in three forms: analog, hybrid, and digital. The Analog Information System (AIS), manifested early in abiogenesis, was expressed in the chiral selection, nucleotide formation, self-assembly, polymerization, encapsulation of polymers, and division of protocells. It created noncoding RNAs by polymerizing [...] Read more.
Prebiotic information systems exist in three forms: analog, hybrid, and digital. The Analog Information System (AIS), manifested early in abiogenesis, was expressed in the chiral selection, nucleotide formation, self-assembly, polymerization, encapsulation of polymers, and division of protocells. It created noncoding RNAs by polymerizing nucleotides that gave rise to the Hybrid Information System (HIS). The HIS employed different species of noncoding RNAs, such as ribozymes, pre-tRNA and tRNA, ribosomes, and functional enzymes, including bridge peptides, pre-aaRS, and aaRS (aminoacyl-tRNA synthetase). Some of these hybrid components build the translation machinery step-by-step. The HIS ushered in the Digital Information System (DIS), where tRNA molecules become molecular architects for designing mRNAs step-by-step, employing their two distinct genetic codes. First, they created codons of mRNA by the base pair interaction (anticodon–codon mapping). Secondly, each charged tRNA transferred its amino acid information to the corresponding codon (codon–amino acid mapping), facilitated by an aaRS enzyme. With the advent of encoded mRNA molecules, the first genes emerged before DNA. With the genetic memory residing in the digital sequences of mRNA, a mapping mechanism was developed between each codon and its cognate amino acid. As more and more codons ‘remembered’ their respective amino acids, this mapping system developed the genetic code in their memory bank. We compared three kinds of biological information systems with similar types of human-made computer systems. Full article
(This article belongs to the Collection Feature Review Papers for Life)
Show Figures

Figure 1

Figure 1
<p>(<b>A</b>) the hierarchical origin of life, viewed as five ascending stages of increasing complexity, showing the biomolecules in the prebiotic world that led to the development of the first cells. These are the cosmic, geological, chemical, information, and biological stages—each higher-level acquired novel emergent properties. In the dark hot environments of hydrothermal crater lake basins, prebiotic synthesis led to first life. (<b>B</b>) the three ways of processing information in life are analog, hybrid, and digital, shown against the hierarchy of life.</p>
Full article ">Figure 2
<p>Cradle of life and its information system. Hydrothermal crater lakes in the Early Archean offered a protective haven for prebiotic synthesis. The boiling water was rich with building blocks of life. On the surface crater basin, lipid vesicles and hydrocarbons were buoyant like tars. The mineral substrates on the floor of the basin acted as catalytic surfaces for the concentration and polymerization of monomers. Convection currents thoroughly mixed the bubbling biotic soup. Some lipid vesicles by convective current went down to the crater floor and stuck to the mineral substrate, encapsulating biopolymers such as RNA and peptides. Hydrothermal vents provide heat, gases, and chemical energy, including thioester and ATP molecules.</p>
Full article ">Figure 3
<p>Chiral selection of monomers such as L-amino acids and D-ribose sugar from the racemic mixture on the mineral substrate floor of the hydrothermal crater vent environment. A short chain of the peptide can be formed by linking a few L-amino acids to each other via peptide bonds by condensation reaction. L-amino acids become monomers of proteins. On the other hand, D-ribose joins with a phosphate molecule to form the backbone of a nucleobase; these three molecules join to form a nucleotide, the monomer of RNA.</p>
Full article ">Figure 4
<p>Amphiphilic compounds like fatty acid can self-assemble into cell-sized vesicles bounded by a membrane. (<b>A</b>) the polar simple fatty acid was likely a major component of the early prebiotic cell membrane due to its ability to form a vesicle. It has a hydrophilic head and a hydrophobic tail. (<b>B</b>) as a monolayer, a micelle can only trap oils, not water, and thus cannot be a precursor to the cell. A bilayer vesicle that trapped water and water-soluble molecules must have given rise to the cell membrane.</p>
Full article ">Figure 5
<p>Condensation reaction on mineral surfaces, where activated monomers drive endergonic polymerization reactions; (<b>A</b>) amino acid structure; all amino acids have the same general configuration: a central carbon bonded to an amino acid functional group, a carboxyl functional group, a hydrogen atom, and a side chain, or R-group. (<b>B</b>–<b>D</b>) How amino acids polymerize to form polypeptides by peptide bonds; (<b>B</b>) the resemblance of an amino acid to a fish helps differentiate its parts. The three amino acids chosen as examples are incredibly similar: each possesses a carboxylic acid group (the ‘tail’) and an amino group (the ‘head’). However, they differ in the ‘dorsal fin’ (R-group of amino acid), which determines the kind of amino acid (here, alanine, glycine, and serine). (<b>C</b>) three molecules of amino acids can polymerize into a polypeptide by linking the amino group of one with the carboxylic acid group of another. This reaction forms a water molecule through the combination of a hydrogen ion (H<sup>+</sup>) discarded from the carboxyl group and a hydroxyl group (OH<sup>_</sup>) discarded from the amino group). (<b>D</b>) shows how a longer chain of amino acids (i.e., a polypeptide) can be formed by removing a water molecule from each link; mRNA-directed protein molecule is also formed similarly by linking amino acids in ribosome during translation. (<b>E</b>) nucleotides can join into an RNA molecule by linking the sugar (S) and phosphate (P) molecules with the backbone of the ribonucleotide bases (B). (<b>E</b>) the linking of nucleotides into RNA was accomplished by dehydration; (<b>F</b>) shows the three components of a nucleotide (phosphate group, sugar, and nitrogenous base) in more detail.</p>
Full article ">Figure 6
<p>Two possible models of the encapsulation of polymers by simple fatty acid membranes on the mineral surface. In model (<b>A</b>), both RNAs and polypeptides are brought together in the same vesicle. In model (<b>B</b>), RNA and peptides are encapsulated separately on the crater basin, then fused in the aqueous environment.</p>
Full article ">Figure 7
<p>Primitive protocell enclosing assemblages of peptide and RNA molecules. (<b>A</b>) Encapsulated polymers such as peptides and RNA and prebiotic soup to create primitive cytoplasm. (<b>B</b>) Some peptides were inserted into the lipid bilayer to enhance permeability in the protocell. The peptides would produce ion-conducting channels through the bilayers that allow phosphate, thioester, ATP, and other nutrients such as amino acids to enter the cell. Molecular crowding inside primitive cytoplasm would encourage symbiotic relations between peptides and RNAs.</p>
Full article ">Figure 8
<p>Fusion and fission of lipid bilayers with inserted peptide molecule (see <a href="#life-12-00834-f007" class="html-fig">Figure 7</a> for explanation). The peptide channels allowed nutrients, lipid components, and energy from the environment to enter protocells by diffusion for growth and division. These protocells form flexible, semi-permeable vesicles, capable of dividing into two such daughter vesicles or of joining with another without any moment of losing their structural continuity. Unlike living cells, the division of protocells is asymmetric, where daughter cells might inherit an unequal amount of cytoplasmic content. The transfer of information from parent to daughter cells is vertical. The cellular division of first cells inherited this property of protocells, but DNA replication created identical daughter cells.</p>
Full article ">Figure 9
<p>Hierarchical evolution of the Analog Information System (AIS) in the early stage of peptide/RNA world. The most basic AIS is termed ‘Molecular Preference AIS’. The higher-level stage AIS is built upon the lower-level AIS. For example, the next stage of AIS, the wet–dry AIS, subsumes the molecular preference AIS, and so on.</p>
Full article ">Figure 10
<p>(<b>A</b>) replication of an RNA molecule by base-pairing. Left: the original RNA strand acts as a template to make a complementary strand by base-pairing. Right: this complementary RNA strand itself acts as a template, forming an RNA strand of the original sequence. (<b>B</b>) Although RNA is a single-stranded molecule, it can form a secondary hairpin structure of ribozyme. (<b>C</b>) Hammerhead ribozyme, like protein, can create tertiary structures and catalyze reactions; the tertiary structure can have both Watson–Crick and non-canonical base pairs.</p>
Full article ">Figure 11
<p>The origin of three components of translation machinery from the hairpin structure of ribozyme with a stem and loop: pre-tRNA molecules (<b>A</b>–<b>D</b>), bridge peptide (<b>E</b>), and ribosome (<b>F</b>). (<b>A</b>,<b>B</b>) The hairpin structure of two ribozymes, each with a loop and a stem. (<b>C</b>) The ligation or duplication of the hairpin structures may give rise to a double hairpin structure, forming a T-hairpin loop and D-hairpin loop with an anticodon (ANT) site between the two stems. (<b>D</b>) A schematic, simplified diagram of the pre-tRNA molecule showing the anticodon site and amino acid attachment site. (<b>E</b>) The hairpin ribozyme structure with a stem and loop and its activating enzyme, the bridge peptide. The amino acid is attached to its free oligonucleotide end by the bridge peptide. (<b>F</b>) Ribosome, a hybrid ribonucleoprotein complex, decodes the message of mRNA to synthesize a small protein chain. It is a decoder of digital information to analog information (modified from [<a href="#B40-life-12-00834" class="html-bibr">40</a>]).</p>
Full article ">Figure 12
<p>(<b>A</b>) The evolution of a tRNA molecule from a precursor pre-tRNA molecule (<b>A</b>,<b>B</b>) by gene duplication. (<b>C</b>) The secondary structure of a tRNA molecule could have been created by ligation of two half-sized pre-tRNA structures. Now a full-length tRNA structure looks like a cloverleaf; its anticodon end forms a complementary base pair with the codon of mRNA; (<b>D</b>) a simplified and schematic diagram of the tRNA molecule showing the site of the anticodon. (<b>E</b>) The cloverleaf secondary structure of tRNA could be folded into an L-shaped tertiary structure; it shows the aminoacylation site at the CCA end. The minihelix region (half domain of tRNA with the amino acid attachment site) interacts with the conserved domain of aaRS for amino acid activation. The other half of tRNA interacts with the non-conserved domain of aaRS for specific recognition of an anticodon (modified from [<a href="#B40-life-12-00834" class="html-bibr">40</a>]).</p>
Full article ">Figure 13
<p>Hierarchical emergence of Hybrid Information System (HIS) during the early stage of peptide/RNA world. The most basic HIS is termed ‘RNA Template HIS’. The higher level (stage) HIS is built upon the lower level HIS. For example, the next stage HIS, the Ribozymal HIS, subsumes the RNA template HIS and so on.</p>
Full article ">Figure 14
<p>Creation of codons by pre-tRNA molecules step by step. (<b>A</b>) GADV amino acids govern the origin of codons via pre-tRNAs; anticodon of a pre-tRNA molecule hybridizing with the corresponding nucleotide available in the prebiotic soup to form a codon strand; each codon developed a memory for a specific amino acid. The four amino acids, glycine (G), alanine (A), aspartic acid (D), and valine (V), were available in the abiotic stage. (<b>B</b>) Codons, thus created by pre-tRNAs, began to link to form a strand of pre-mRNA with coding sequence; (<b>C</b>) Pre-tRNA and pre-mRNA interactions generated rudimentary translation. In this figure, we offer a specific mapping mechanism between codons and their cognate amino acids that led to rudimentary translation and the genetic code (modified from [<a href="#B40-life-12-00834" class="html-bibr">40</a>]).</p>
Full article ">Figure 15
<p>The encoding properties of tRNA. tRNA played two critical roles in creating and encoding codons corresponding to two different genetic codes. First, it created a codon by Watson–Crick base pair interaction (anticodon–codon mapping). Secondly, each charged tRNA transferred its amino acid information to the corresponding codon (codon–amino acid mapping). Participation of aaRS in the recognition process is an attractive possibility.</p>
Full article ">Figure 16
<p>A four-level hierarchy of Digital Information System (DIS) stages in the peptide/RNA world. The codon reader-acceptor DIS was able to form a codon. The sequence, a memory-based DIS in the next stage, was able to link codons into pre-mRNAs. The codons in pre-mRNA and mRNA were encoded by pre-tRNA and tRNA, respectively. Finally, mRNA was decoded by translation machine to create protein chain.</p>
Full article ">Figure 17
<p>Codon–amino acid mapping and the origin of genes. Encoding codons by charged pre-tRNA and tRNA molecules in the three stages of the genetic code, controlled by the availability of amino acids in hydrothermal crater vent environment. In the GNC code, four pre-mRNA codons specify the four amino acids. In the SNS code, 16 mRNA codons code ten amino acids. In the universal genetic code, 61 mRNA codons designate the 20 amino acids. In the left column of each stage, the white circles represent the uncoded codons, while the blue codons represent encoded codons. Twenty-three to forty-five charged tRNA molecules perform the task of encoding codons.</p>
Full article ">Figure 18
<p>The coevolution of translation machines and the genetic code in three stages: (<b>A</b>) encoding of pre-mRNA molecule by pre-tRNA/pre-aaRS translation machine when GNC code evolved; (<b>B</b>) encoding of short-chain mRNA molecule by tRNA/aaRS translation machine when SNS code appeared; and finally, (<b>C</b>) encoding of long-chain mRNA by tRNA/aaRS/ribosome machine when universal code evolved. With the improvement of the translation machine, the information density of mRNA also increased (modified from [<a href="#B40-life-12-00834" class="html-bibr">40</a>]).</p>
Full article ">Figure 19
<p>(<b>A</b>) (top) Three stages of the evolution of mRNA, translation machines, and genetic code. (<b>a</b>) Decoding of pre-mRNA by pre-tRNA/pre-aaRS machine resulting in the primitive GNC code. (<b>b</b>) Fecoding of short-chain mRNA by tRNA/aaRS machine in the transitional SNS code. (<b>c</b>) Decoding of long-chain mRNA by tRNA/aaRS/ribosome machine in the universal genetic code. Left column of the diagram shows the recruitment of amino acids during the evolution of the genetic code. (<b>B</b>) (bottom) Darwinian evolution began in the peptide/RNA world, an interplay between digital information and its supporting structure, such as a translation machine. The supporting structure is coupled to the information carrier by rules, such as RNA base-pairing and genetic code. The supporting structure is nourished by the chemicals and energy from the hydrothermal vent environment and provides the information carrier positive feedback.</p>
Full article ">Figure 20
<p>In a digital information transmission system, mRNA functions as the encoder of amino acid information and ribosome as a decoder of DIS to AIS to create protein.</p>
Full article ">Figure 21
<p>A Block diagram of the TR-10 analog computer built by Electronic Associates, Inc. EAI’s PACE TR-10, an electronic analog computer.</p>
Full article ">Figure 22
<p>Block diagram of a basic digital computer with a uniprocessor CPU. Black lines indicate data flow, whereas red lines indicate control flow. Arrows indicate the direction of flow [adapted from Lambtron-owned work, CC BY-SA 4.0].</p>
Full article ">Figure 23
<p>Block diagram of an available hybrid computer system.</p>
Full article ">Figure 24
<p>Coevolution of biomolecules with the biological information systems in the peptide/RNA world. An analog information system dominated the early stage of abiogenesis. With the emergence of nucleotides, hybrid information began to emerge. The origin of pre-mRNA and mRNA marked the digital revolution. During the origin of translation and the genetic code, the directionality of information flow from mRNA to proteins emerged.</p>
Full article ">Scheme 1
<p>Twenty primary amino acids in the Genetic Code and their corresponding numerical codons shown by 23 alphabets. This represents the decoding table from mRNA to protein translation. The three letters B, O, and U remain unused.</p>
Full article ">Scheme 2
<p>Universal Genetic code showing numerical codons with corresponding amino acids.</p>
Full article ">Scheme 3
<p>Codon–amino acid mapping in three stages of genetic code using CATI software. In SNS and universal genetic code, the sequence of generating redundancy of codons to amino acids is shown.</p>
Full article ">Scheme 4
<p>Three stages of the DIS, HIS, and AIS during the evolution of the genetic code. In GNC code, pre-mRNA was decoded by a pre-tRNA/pre-aaRS translation machine, creating a polypeptide chain. In SNS code, short-chain mRNA was decoded by a tRNA/aaRS machine, producing short-chain protein. In universal genetic code, long-chain mRNA was decoded by tRNA/aaRS/ribosome machine, manufacturing long-chain protein.</p>
Full article ">Scheme 5
<p>(<b>A</b>) Universal genetic code table shows 64 codons, each corresponding to a specific amino acid or stop signal. The start codon (AUG) is shown in green. Stop codons (UAA, UAG, and UGA) are shown in red. (<b>B</b>) In the genetic code, 20 amino acids are used in protein synthesis showing corresponding codons in redundancy.</p>
Full article ">Figure A1
<p>A screenshot of the webpage showing the button to be pushed.</p>
Full article ">Figure A2
<p>A screenshot of the webpage showing the button to be pushed.</p>
Full article ">Figure A3
<p>A screenshot of the webpage showing the button to be pushed.</p>
Full article ">
30 pages, 2846 KiB  
Review
Gastrointestinal Microbiota and Their Manipulation for Improved Growth and Performance in Chickens
by Shahna Fathima, Revathi Shanmugasundaram, Daniel Adams and Ramesh K. Selvaraj
Foods 2022, 11(10), 1401; https://doi.org/10.3390/foods11101401 - 12 May 2022
Cited by 58 | Viewed by 9730
Abstract
The gut of warm-blooded animals is colonized by microbes possibly constituting at least 100 times more genetic material of microbial cells than that of the somatic cells of the host. These microbes have a profound effect on several physiological functions ranging from energy [...] Read more.
The gut of warm-blooded animals is colonized by microbes possibly constituting at least 100 times more genetic material of microbial cells than that of the somatic cells of the host. These microbes have a profound effect on several physiological functions ranging from energy metabolism to the immune response of the host, particularly those associated with the gut immune system. The gut of a newly hatched chick is typically sterile but is rapidly colonized by microbes in the environment, undergoing cycles of development. Several factors such as diet, region of the gastrointestinal tract, housing, environment, and genetics can influence the microbial composition of an individual bird and can confer a distinctive microbiome signature to the individual bird. The microbial composition can be modified by the supplementation of probiotics, prebiotics, or synbiotics. Supplementing these additives can prevent dysbiosis caused by stress factors such as infection, heat stress, and toxins that cause dysbiosis. The mechanism of action and beneficial effects of probiotics vary depending on the strains used. However, it is difficult to establish a relationship between the gut microbiome and host health and productivity due to high variability between flocks due to environmental, nutritional, and host factors. This review compiles information on the gut microbiota, dysbiosis, and additives such as probiotics, postbiotics, prebiotics, and synbiotics, which are capable of modifying gut microbiota and elaborates on the interaction of these additives with chicken gut commensals, immune system, and their consequent effects on health and productivity. Factors to be considered and the unexplored potential of genetic engineering of poultry probiotics in addressing public health concerns and zoonosis associated with the poultry industry are discussed. Full article
(This article belongs to the Special Issue Postbiotics: Emerging Applications in Food Field)
Show Figures

Figure 1

Figure 1
<p>Regional abundance and diversity of gastrointestinal microbiota of chicken. Created with BioRender.com (26 March 2022).</p>
Full article ">Figure 2
<p>Dysbiosis induced by different factors alters the gastrointestinal homeostasis causing impaired epithelial barrier function and systemic inflammation. Created with BioRender.com (26 March 2022).</p>
Full article ">Figure 3
<p>Postbiotics are soluble low molecular weight metabolites or cell lysis products derived from live or inactivated probiotic bacteria which when administered in adequate quantities demonstrate beneficial effects on host health. Created with Biorender.com (4 April 2022).</p>
Full article ">Figure 4
<p>Prebiotics are polysaccharides or oligosaccharides capable of resisting digestion and absorption in the proximal intestine and is selectively fermented by caecal and colonic bacteria such as <span class="html-italic">Lactobacillus</span> and <span class="html-italic">Bifidobacterium</span>, thus increasing their abundance in host gut. Prebiotics act as decoy receptors for the binding of pathogens, thus preventing their attachment to the host intestinal cells. Prebiotics also serve as a substrate for the production of SCFAs which serve as energy source for the intestinal epithelial cells. Created with Biorender.com (26 April 2022).</p>
Full article ">Figure 5
<p>Synbiotics are essentially a combination of probiotics and prebiotics where the probiotic component is specifically fermentable by the prebiotic component and thus helps in establishing a stable population in the host gut. Created with Biorender.com (28 April 2022).</p>
Full article ">
39 pages, 2663 KiB  
Article
Thermodynamic and Kinetic Sequence Selection in Enzyme-Free Polymer Self-Assembly inside a Non-equilibrium RNA Reactor
by Tobias Göppel, Joachim H. Rosenberger, Bernhard Altaner and Ulrich Gerland
Life 2022, 12(4), 567; https://doi.org/10.3390/life12040567 - 10 Apr 2022
Cited by 5 | Viewed by 3213
Abstract
The RNA world is one of the principal hypotheses to explain the emergence of living systems on the prebiotic Earth. It posits that RNA oligonucleotides acted as both carriers of information as well as catalytic molecules, promoting their own replication. However, it does [...] Read more.
The RNA world is one of the principal hypotheses to explain the emergence of living systems on the prebiotic Earth. It posits that RNA oligonucleotides acted as both carriers of information as well as catalytic molecules, promoting their own replication. However, it does not explain the origin of the catalytic RNA molecules. How could the transition from a pre-RNA to an RNA world occur? A starting point to answer this question is to analyze the dynamics in sequence space on the lowest level, where mononucleotide and short oligonucleotides come together and collectively evolve into larger molecules. To this end, we study the sequence-dependent self-assembly of polymers from a random initial pool of short building blocks via templated ligation. Templated ligation requires two strands that are hybridized adjacently on a third strand. The thermodynamic stability of such a configuration crucially depends on the sequence context and, therefore, significantly influences the ligation probability. However, the sequence context also has a kinetic effect, since non-complementary nucleotide pairs in the vicinity of the ligation site stall the ligation reaction. These sequence-dependent thermodynamic and kinetic effects are explicitly included in our stochastic model. Using this model, we investigate the system-level dynamics inside a non-equilibrium ‘RNA reactor’ enabling a fast chemical activation of the termini of interacting oligomers. Moreover, the RNA reactor subjects the oligomer pool to periodic temperature changes inducing the reshuffling of the system. The binding stability of strands typically grows with the number of complementary nucleotides forming the hybridization site. While shorter strands unbind spontaneously during the cold phase, larger complexes only disassemble during the temperature peaks. Inside the RNA reactor, strand growth is balanced by cleavage via hydrolysis, such that the oligomer pool eventually reaches a non-equilibrium stationary state characterized by its length and sequence distribution. How do motif-dependent energy and stalling parameters affect the sequence composition of the pool of long strands? As a critical factor for self-enhancing sequence selection, we identify kinetic stalling due to non-complementary base pairs at the ligation site. Kinetic stalling enables cascades of self-amplification that result in a strong reduction of occupied states in sequence space. Moreover, we discuss the significance of the symmetry breaking for the transition from a pre-RNA to an RNA world. Full article
(This article belongs to the Special Issue Organic Chemical Evolution regarding the Origin(s) of Life)
Show Figures

Figure 1

Figure 1
<p>Schematic illustration of the dynamics inside the RNA reactor. The elementary processes are hybridization, dehybridization, ligation on the template, and hydrolysis with corresponding elementary rates <math display="inline"><semantics> <msub> <mi>k</mi> <mi>on</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>k</mi> <mi>off</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>k</mi> <mi>lig</mi> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mi>k</mi> <mi>cut</mi> </msub> </semantics></math>. The elementary rates <math display="inline"><semantics> <msub> <mi>k</mi> <mi>on</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>k</mi> <mi>off</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>k</mi> <mi>lig</mi> </msub> </semantics></math> are functions of the sequence context: (<b>a</b>) Strands have a binary sequence and are directed. <span class="html-italic">L</span> denotes their length. (<b>b</b>) When two molecules collide, they can form <math display="inline"><semantics> <mi>χ</mi> </semantics></math> different hybridization complexes. (<b>c</b>) Hybridization sites within complexes (horizontal interfaces) can contain mismatches. Two strands (− and + strand) located adjacently on another strand may get joined covalently via templated ligation. The speed of the ligation reaction depends on the complementarity <math display="inline"><semantics> <mi>κ</mi> </semantics></math> of the nucleotide pairs at the <math display="inline"><semantics> <mrow> <mo>±</mo> <mn>1</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>±</mo> <mn>2</mn> </mrow> </semantics></math> position (red box). Non-complementary pairings lead to kinetic stalling. (<b>d</b>) The stability of a hybridization site is governed by the hybridization energy <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>G</mi> <mi>hyb</mi> </msub> </mrow> </semantics></math>. <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>G</mi> <mi>hyb</mi> </msub> </mrow> </semantics></math> is obtained by summing over stacking energies <math display="inline"><semantics> <mi>γ</mi> </semantics></math> associated with nearest-neighbor blocks (purple box) and considering terminal nucleotide pairs. <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>G</mi> <mi>hyb</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mi>γ</mi> </semantics></math> depend on the structural and sequential context. Mismatches weaken the binding. (<b>e</b>,<b>f</b>) Covalent bonds within single strands or single-stranded segments may get cleaved via hydrolysis at a constant rate. The resulting unactivated strand termini are assumed to be rapidly reactivated.</p>
Full article ">Figure 2
<p>(<b>a</b>) Schematic illustration of the time evolution of the non-equilibrium RNA reactor. The reactor is initialized symmetrically with mononucleotides and a few dimers such that the amounts of <span class="html-italic">X</span> and <span class="html-italic">Y</span> nucleotides are equal and that all four dimer sequences have the same concentrations (see <a href="#sec3dot1-life-12-00567" class="html-sec">Section 3.1</a>). Within the RNA reactor, oligomers grow via templated ligation and degrade via hydrolysis. Eventually, the sequence pool converges to a non-equilibrium stationary state characterized by its length and sequence distribution (see <a href="#life-12-00567-f001" class="html-fig">Figure 1</a>); (<b>b</b>) To characterize the dynamics in sequence space, we introduce the zebraness <math display="inline"><semantics> <mi>ζ</mi> </semantics></math> on the level of single strands and the system-level zebraness <span class="html-italic">Z</span>. The zebraness <math display="inline"><semantics> <mi>ζ</mi> </semantics></math> of a single strand is the fraction of zebra motifs, i.e., alternating binary motifs contained in the strand. In contrast, the system-level zebraness <span class="html-italic">Z</span> measures how zebra-like, i.e., alternating or homogeneous, the pool is as a whole. <span class="html-italic">Z</span> corresponds to the total number of zebra motifs spread over all strands normalized with respect to the overall number of binary motifs within all strands present in the reactor.</p>
Full article ">Figure 3
<p>Mean length <math display="inline"><semantics> <mover> <mi>L</mi> <mo>¯</mo> </mover> </semantics></math> and system-level zebraness <span class="html-italic">Z</span> as functions of time for <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>1</mn> </msub> <mo>=</mo> <msub> <mi>σ</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> and various <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>γ</mi> </mrow> </semantics></math>. (<b>a</b>) A sharp increase of <math display="inline"><semantics> <mover> <mi>L</mi> <mo>¯</mo> </mover> </semantics></math> appears at <math display="inline"><semantics> <mover accent="true"> <mi>t</mi> <mo>^</mo> </mover> </semantics></math>. For <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>γ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, the dashed line corresponds to <math display="inline"><semantics> <mover accent="true"> <mi>t</mi> <mo>^</mo> </mover> </semantics></math> resulting from the formal definition, whereas the dotted line is the prediction obtained from Equation (<a href="#FD19-life-12-00567" class="html-disp-formula">19</a>). For <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>γ</mi> <mo>&lt;</mo> <mn>0</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mover> <mi>L</mi> <mo>¯</mo> </mover> </semantics></math> reaches a maximum before decaying gradually to the stationary value. The inset shows the steady-state length distributions. (<b>b</b>) If there is no energetic bias, i.e., <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>γ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, no distinct patterns emerge in sequence space, and hence <math display="inline"><semantics> <mrow> <mi>Z</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math> (see also <a href="#app4-life-12-00567" class="html-app">Appendix D</a>). If an energetic bias <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>γ</mi> <mo>&lt;</mo> <mn>0</mn> </mrow> </semantics></math> is applied, <span class="html-italic">Z</span> grows initially and then decays when <math display="inline"><semantics> <mrow> <mover> <mi>L</mi> <mo>¯</mo> </mover> <mo>≈</mo> <mn>7</mn> </mrow> </semantics></math>. The final value is slightly above the random state <math display="inline"><semantics> <mrow> <mi>Z</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math> and below the simple thermodynamic estimate <math display="inline"><semantics> <msup> <mrow> <mi>Z</mi> </mrow> <mo>*</mo> </msup> </semantics></math> (see Equation (<a href="#FD22-life-12-00567" class="html-disp-formula">22</a>)). (<b>c</b>) Single realizations of the dynamics for <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>γ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> behave similar to ensemble average. Strong fluctuations for small times stem from low numbers of strands with <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>&gt;</mo> <mn>1</mn> </mrow> </semantics></math>. (<b>d</b>) The fraction of mismatches <span class="html-italic">m</span> first decreases and then increases as the mean length becomes longer. (<b>e</b>) The fraction of concealed mismatches, i.e., mismatches not affected by energetic discrimination grows simultaneous with the mean length. (<b>f</b>) Over time, concealed erroneous ligations become frequent and destroy the initial sequence bias.</p>
Full article ">Figure 4
<p>Evolution of mean length <math display="inline"><semantics> <mover> <mi>L</mi> <mo>¯</mo> </mover> </semantics></math>, sequence order parameter <math display="inline"><semantics> <mi>P</mi> </semantics></math>, and system-level zebraness <span class="html-italic">Z</span> in a kinetic stalling scenario without energetic bias, i.e., <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>γ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>. For reference, we show the <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>1</mn> </msub> <mo>=</mo> <msub> <mi>σ</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> curves (gray). (<b>a</b>) For <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>1</mn> </msub> <mo>=</mo> <msub> <mi>σ</mi> <mn>2</mn> </msub> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mover> <mi>L</mi> <mo>¯</mo> </mover> </semantics></math> shows two distinct growth phases. While the first one is rapid, the second one is slow. Relaxation to the steady-state for <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.067</mn> </mrow> </semantics></math> appears much later (see inset). For <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>γ</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, the dashed line corresponds to <math display="inline"><semantics> <mover accent="true"> <mi>t</mi> <mo>^</mo> </mover> </semantics></math> resulting from the formal definition, whereas the dotted line is the prediction. (<b>b</b>) The bias for alternating or homogeneous patterns established in the first growth phase becomes amplified during the second growth phase. For strong stalling (<math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>1</mn> </msub> <mo>≤</mo> <mn>0.067</mn> </mrow> </semantics></math>), the final pool comprises either pure zebra or fully homogeneous sequences. (<b>c</b>) The symmetry of the initial state is broken spontaneously. For <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, equal fractions of realizations evolve to the zebra or homogeneous state; (<b>c</b>–<b>f</b>) Dynamics of mismatches, concealed mismatches, and concealed erroneous ligations for <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mn>0.05</mn> <mo>,</mo> <mn>0.1</mn> </mrow> </semantics></math>. For details, see the main text.</p>
Full article ">Figure 5
<p>Left column: scenario with <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, right column: scenario with <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> (<b>a</b>) The mean length <math display="inline"><semantics> <mover> <mi>L</mi> <mo>¯</mo> </mover> </semantics></math> grows again in two steps. The stronger the bias, the earlier the relaxation to the stationary value. For <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>γ</mi> <mo>=</mo> <mo>−</mo> <mn>0.3</mn> </mrow> </semantics></math>, the dashed line corresponds to <math display="inline"><semantics> <mover accent="true"> <mi>t</mi> <mo>^</mo> </mover> </semantics></math> resulting from the formal definition, whereas the dotted line is the prediction (same for (<b>c</b>)). (<b>b</b>) Pronounced zebra patterns emerge during the first growth phase. The patterns become pure during the second growth phase. (<b>c</b>) A gradual increase or decay follows the rapid growth phase. The steady-state value of <math display="inline"><semantics> <mover> <mi>L</mi> <mo>¯</mo> </mover> </semantics></math> correlates with the strength of the energetic bias. (<b>d</b>) While <math display="inline"><semantics> <mover> <mi>L</mi> <mo>¯</mo> </mover> </semantics></math> slightly increases (decreases), <span class="html-italic">Z</span> also increases (decreases). The sequence pool in the stationary-state shows mixed patterns dominated by zebra motifs. The fraction of zebra motifs depends on the energetic bias. (<b>a</b>–<b>d</b>) For reference, we also show the sequence order parameter <math display="inline"><semantics> <mi>P</mi> </semantics></math> for the <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>γ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> curves (gray).</p>
Full article ">Figure A1
<p>(<b>a</b>) A duplex <math display="inline"><semantics> <msub> <mi>C</mi> <mn>1</mn> </msub> </semantics></math> with two dangling ends and a single strand <math display="inline"><semantics> <msub> <mi>C</mi> <mn>2</mn> </msub> </semantics></math> form a new triplex <math display="inline"><semantics> <msub> <mi>C</mi> <mn>3</mn> </msub> </semantics></math> with a blunt end. <math display="inline"><semantics> <msub> <mi>C</mi> <mn>3</mn> </msub> </semantics></math> has no ligation site. (<b>b</b>) Two single strands <math display="inline"><semantics> <msub> <mi>C</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>C</mi> <mn>2</mn> </msub> </semantics></math> react to a duplex <math display="inline"><semantics> <msub> <mi>C</mi> <mn>3</mn> </msub> </semantics></math> displaying a mismatch and two dangling ends. (<b>c</b>) Two duplexes <math display="inline"><semantics> <msub> <mi>C</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>C</mi> <mn>2</mn> </msub> </semantics></math> hybridize to new complex <math display="inline"><semantics> <msub> <mi>C</mi> <mn>3</mn> </msub> </semantics></math> featuring two new ligation sites. (<b>d</b>) A mononucleotide <math display="inline"><semantics> <msub> <mi>C</mi> <mn>1</mn> </msub> </semantics></math> hybridizes onto a duplex <math display="inline"><semantics> <msub> <mi>C</mi> <mn>2</mn> </msub> </semantics></math>. The new triplex has a new ligation site. (<b>e</b>) We need to update the channel factor <math display="inline"><semantics> <mi>χ</mi> </semantics></math> to renew the dehybridization rate <math display="inline"><semantics> <msub> <mi>k</mi> <mi>off</mi> </msub> </semantics></math> associated with the hybridization site that already existed before the binding of the monomer. To this end, we virtually dissolve this hybridization site and directly reassemble the complex and recount the possible reaction channels and obtain a new (integer) value for the channel factor <math display="inline"><semantics> <mi>χ</mi> </semantics></math> (see main text).</p>
Full article ">Figure A2
<p>Relative entropies of the distributions of (sub)motifs of size four (<b>a</b>); six (<b>b</b>); and eight (<b>c</b>) as a function of time. Green curves: data obtained from simulations of the full model dynamics. Blue curves: data generated by the corresponding random process. At every time point, the number of (sub) motifs generated by the random process equals the number of (sub)motifs found in the simulation output. For small times, correlations in sequence lead to an increased relative entropy in the model dynamics. For large times, model dynamics and random processes yield similar results. The insets show the normalized frequency of (sub)motifs sorted by abundance in for the last time point.</p>
Full article ">Figure A3
<p>(<b>a</b>) We formally define the onset of growth <math display="inline"><semantics> <mover accent="true"> <mi>t</mi> <mo>^</mo> </mover> </semantics></math> (dashed line) by intersecting the tangents to <math display="inline"><semantics> <mrow> <mover> <mi>L</mi> <mo>¯</mo> </mover> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> and the point where the increase is steepest (dotted lines). (<b>b</b>) <math display="inline"><semantics> <msub> <mi>t</mi> <mi>high</mi> </msub> </semantics></math> is defined as the time point where higher-order ligations become more frequent than first-order ligations. Our estimate <math display="inline"><semantics> <msub> <mi>t</mi> <mi>est</mi> </msub> </semantics></math> obtained from Equation (<a href="#FD65-life-12-00567" class="html-disp-formula">A39</a>) matches <math display="inline"><semantics> <msub> <mi>t</mi> <mi>high</mi> </msub> </semantics></math> well (compare dashed lines). Curves are normalized such that, on average, there is one ligation per time interval in the steady-state. (<b>c</b>) The initial exponential growth of the dimer concentration is described by Equation (<a href="#FD63-life-12-00567" class="html-disp-formula">A37</a>) (dotted line). The dimer concentration has a maximum at <math display="inline"><semantics> <msub> <mi>t</mi> <mi>high</mi> </msub> </semantics></math>.</p>
Full article ">Figure A4
<p>A monomer hybridized adjacent to matching primer terminus (<b>a</b>) has a lower dehybridization rate than a monomer bound next to a non-complementary terminus (<b>b</b>), leading to thermodynamic stalling. Moreover, kinetic stalling reduces the ligation rate.</p>
Full article ">
Back to TopTop