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Our universe may have started by Qubit decoherence:
In quantum computers, qubits have all their states undefined during calculation and become defined as output (“decoherence”). We study the transition from an uncontrolled, chaotic quantum vacuum (“before”) to a clearly interacting “real world”. In such a cosmology, the Big Bang singularity is replaced by a condensation event of interacting strings. This triggers a crystallization process. This avoids inflation, not fitting current observations: increasing long-range interactions limit growth and crystal symmetries ensure the same laws of nature and basic symmetries over the whole crystal. Tiny mis-arrangements provide nuclei of superclusters and galaxies and crystal structure allows arrangement of dark (halo regions) and normal matter (galaxy nuclei) for galaxy formation. Crystals come and go: an evolutionary cosmology is explored: entropic forces from the quantum soup “outside” of the crystal try to dissolve it. This corresponds to dark energy and leads to a “big rip” in 70 Gigayears. Selection for best growth and condensation events over generations of crystals favors multiple self-organizing processes within the crystal including life or even conscious observers in our universe. Philosophically this theory shows harmony with nature and replaces absurd perspectives of current cosmology.
Independent of cosmology, we suggest that a “real world” (so our everyday macroscopic world) happens only inside a crystal. “Outside” there is wild quantum foam and superposition of all possibilities. In our crystallized world the vacuum no longer boils but is cooled down by the crystallization event, space-time exists and general relativity holds. Vacuum energy becomes 10**20 smaller, exactly as observed in our everyday world. We live in a “solid” state, within a crystal, the n quanta which build our world have all their different m states nicely separated. There are only nm states available for this local “multiverse”. The arrow of entropy for each edge of the crystal forms one fate, one world-line or clear development of our world, while layers of the crystal are different system states. Mathematical leads from loop quantum gravity (LQG) point to required interactions and potentials. Interaction potentials for strings or loop quanta of any dimension allow a solid, decoherent state of quanta challenging to calculate. However, if we introduce here the heuristic that any type of physical interaction of strings corresponds just to a type of calculation, there is already since 1898 the Hurwitz theorem showing that then only 1D, 2D, 4D and 8D (octonions) allow complex or hypercomplex number calculations. No other hypercomplex numbers and hence dimensions or symmetries are possible to allow calculations without yielding divisions by zero. However, the richest solution allowed by the Hurwitz theorem, octonions, is actually the observed symmetry of our universe, E8. Standard physics such as condensation, crystallization and magnetization but also solid-state physics and quantum computing allow us to show an initial mathematical treatment of our new theory by LQG to describe the cosmological state transformations by equations, and, most importantly, point out routes to parametrization of free parameters looking at testable phenomena, experiments and formulas that describe processes of crystallization, protein folding, magnetization, solid-state physics and quantum computing. This is presented here for LQG, for string theory it would be more elegant but was too demanding to be shown here.
Note: While my previous Opus server preprint “A new cosmology of a crystallization process (decoherence) from the surrounding quantum soup provides heuristics to unify general relativity and quantum physics by solid state physics” (https://doi.org/10.25972/OPUS-23076) deals with the same topics and basic formulas, this new version is improved: clearer in title, better introduction, more stringent in its mathematics and improved discussion of the implications including quantum computing, hints for parametrization and connections to LQG and other current cosmological efforts.
This 5th of June 2021 version is again an OPUS preprint, but this will next be edited for Archives https://arxiv.org.
A viral infection involves entry and replication of viral nucleic acid in a host organism, subsequently leading to biochemical and structural alterations in the host cell. In the case of SARS-CoV-2 viral infection, over-activation of the host immune system may lead to lung damage. Albeit the regeneration and fibrotic repair processes being the two protective host responses, prolonged injury may lead to excessive fibrosis, a pathological state that can result in lung collapse. In this review, we discuss regeneration and fibrosis processes in response to SARS-CoV-2 and provide our viewpoint on the triggering of alveolar regeneration in coronavirus disease 2019 (COVID-19) patients.
Serine/threonine kinase PknB and its corresponding phosphatase Stp are important regulators of many cell functions in the pathogen S. aureus. Genome-scale gene expression data of S. aureus strain NewHG (sigB\(^+\)) elucidated their effect on physiological functions. Moreover, metabolic modelling from these data inferred metabolic adaptations. We compared wild-type to deletion strains lacking pknB, stp or both. Ser/Thr phosphorylation of target proteins by PknB switched amino acid catabolism off and gluconeogenesis on to provide the cell with sufficient components. We revealed a significant impact of PknB and Stp on peptidoglycan, nucleotide and aromatic amino acid synthesis, as well as catabolism involving aspartate transaminase. Moreover, pyrimidine synthesis was dramatically impaired by stp deletion but only slightly by functional loss of PknB. In double knockouts, higher activity concerned genes involved in peptidoglycan, purine and aromatic amino acid synthesis from glucose but lower activity of pyrimidine synthesis from glucose compared to the wild type. A second transcriptome dataset from S. aureus NCTC 8325 (sigB\(^−\)) validated the predictions. For this metabolic adaptation, PknB was found to interact with CdaA and the yvcK/glmR regulon. The involved GlmR structure and the GlmS riboswitch were modelled. Furthermore, PknB phosphorylation lowered the expression of many virulence factors, and the study shed light on S. aureus infection processes.
We observed substantial differences in predicted Major Histocompatibility Complex II (MHCII) epitope presentation of SARS-CoV-2 proteins for different populations but only minor differences in predicted MHCI epitope presentation. A comparison of this predicted epitope MHC-coverage revealed for the early phase of infection spread (till day 15 after reaching 128 observed infection cases) highly significant negative correlations with the case fatality rate. Specifically, this was observed in different populations for MHC class II presentation of the viral spike protein (p-value: 0.0733 for linear regression), the envelope protein (p-value: 0.023), and the membrane protein (p-value: 0.00053), indicating that the high case fatality rates of COVID-19 observed in some countries seem to be related with poor MHC class II presentation and hence weak adaptive immune response against these viral envelope proteins. Our results highlight the general importance of the SARS-CoV-2 structural proteins in immunological control in early infection spread looking at a global census in various countries and taking case fatality rate into account. Other factors such as health system and control measures become more important after the early spread. Our study should encourage further studies on MHCII alleles as potential risk factors in COVID-19 including assessment of local populations and specific allele distributions.
Climate plants are critical to prevent global warming as all efforts to save carbon dioxide are too slow and climate disasters on the rise. For best carbon dioxide harvesting we compare algae, trees and crop plants and use metagenomic analysis of environmental samples. We compare different pathways, carbon harvesting potentials of different plants as well as synthetic modifications including carbon dioxide flux balance analysis. For implementation, agriculture and modern forestry are important.
Plant hormones are small regulatory molecules that exert pharmacological actions in mammalian cells such as anti-oxidative and pro-metabolic effects. Kinetin belongs to the group of plant hormones cytokinin and has been associated with modulatory functions in mammalian cells. The mammalian adenosine receptor (A2a-R) is known to modulate multiple physiological responses in animal cells. Here, we describe that kinetin binds to the adenosine receptor (A2a-R) through the Asn253 residue in an adenosine dependent manner. To harness the beneficial effects of kinetin for future human use, we assess its acute toxicity by analyzing different biochemical and histological markers in rats. Kinetin at a dose below 1 mg/kg had no adverse effects on the serum level of glucose or on the activity of serum alanine transaminase (ALT) or aspartate aminotransferase (AST) enzymes in the kinetin treated rats. Whereas, creatinine levels increased after a kinetin treatment at a dose of 0.5 mg/kg. Furthermore, 5 mg/kg treated kinetin rats showed normal renal corpuscles, but a mild degeneration was observed in the renal glomeruli and renal tubules, as well as few degenerated hepatocytes were also observed in the liver. Kinetin doses below 5 mg/kg did not show any localized toxicity in the liver and kidney tissues. In addition to unraveling the binding interaction between kinetin and A2a-R, our findings suggest safe dose limits for the future use of kinetin as a therapeutic and modulatory agent against various pathophysiological conditions.
Stapylococcus aureus colonises the nose of healthy individuals but can also cause a wide range of infections. Amino acid (AA) synthesis and their availability is crucial to adapt to conditions encountered in vivo. Most S. aureus genomes comprise all genes required for AA biosynthesis. Nevertheless, different strains require specific sets of AAs for growth. In this study we show that regulation inactivates pathways under certain conditions which result in these observed auxotrophies. We analyzed in vitro and modeled in silico in a Boolean semiquantitative model (195 nodes, 320 edges) the regulatory impact of stringent response (SR) on AA requirement in S. aureus HG001 (wild-type) and in mutant strains lacking the metabolic regulators RSH, CodY and CcpA, respectively. Growth in medium lacking single AAs was analyzed. Results correlated qualitatively to the in silico predictions of the final model in 92% and quantitatively in 81%. Remaining gaps in our knowledge are evaluated and discussed. This in silico model is made fully available and explains how integration of different inputs is achieved in SR and AA metabolism of S. aureus. The in vitro data and in silico modeling stress the role of SR and central regulators such as CodY for AA metabolisms in S. aureus.
The present study reports the synthesis of new purine bioisosteres comprising a pyrazolo[3,4-d]pyrimidine scaffold linked to mono-, di-, and trimethoxy benzylidene moieties through hydrazine linkages. First, in silico docking experiments of the synthesized compounds against Bax, Bcl-2, Caspase-3, Ki67, p21, and p53 were performed in a trial to rationalize the observed cytotoxic activity for the tested compounds. The anticancer activity of these compounds was evaluated in vitro against Caco-2, A549, HT1080, and Hela cell lines. Results revealed that two (5 and 7) of the three synthesized compounds (5, 6, and 7) showed high cytotoxic activity against all tested cell lines with IC50 values in the micro molar concentration. Our in vitro results show that there is no significant apoptotic effect for the treatment with the experimental compounds on the viability of cells against A549 cells. Ki67 expression was found to decrease significantly following the treatment of cells with the most promising candidate: drug 7. The overall results indicate that these pyrazolopyrimidine derivatives possess anticancer activity at varying doses. The suggested mechanism of action involves the inhibition of the proliferation of cancer cells.
Whereas the role of calcium ions (Ca\(^{2+}\)) in plant signaling is well studied, the physiological significance of pH‐changes remains largely undefined.
Here we developed CapHensor, an optimized dual‐reporter for simultaneous Ca\(^{2+}\) and pH ratio‐imaging and studied signaling events in pollen tubes (PTs), guard cells (GCs), and mesophyll cells (MCs). Monitoring spatio‐temporal relationships between membrane voltage, Ca\(^{2+}\)‐ and pH‐dynamics revealed interconnections previously not described.
In tobacco PTs, we demonstrated Ca\(^{2+}\)‐dynamics lag behind pH‐dynamics during oscillatory growth, and pH correlates more with growth than Ca\(^{2+}\). In GCs, we demonstrated abscisic acid (ABA) to initiate stomatal closure via rapid cytosolic alkalization followed by Ca2+ elevation. Preventing the alkalization blocked GC ABA‐responses and even opened stomata in the presence of ABA, disclosing an important pH‐dependent GC signaling node. In MCs, a flg22‐induced membrane depolarization preceded Ca2+‐increases and cytosolic acidification by c. 2 min, suggesting a Ca\(^{2+}\)/pH‐independent early pathogen signaling step. Imaging Ca2+ and pH resolved similar cytosol and nuclear signals and demonstrated flg22, but not ABA and hydrogen peroxide to initiate rapid membrane voltage‐, Ca\(^{2+}\)‐ and pH‐responses.
We propose close interrelation in Ca\(^{2+}\)‐ and pH‐signaling that is cell type‐ and stimulus‐specific and the pH having crucial roles in regulating PT growth and stomata movement.
Circadian endogenous clocks of eukaryotic organisms are an established and rapidly developing research field. To investigate and simulate in an effective model the effect of external stimuli on such clocks and their components we developed a software framework for download and simulation. The application is useful to understand the different involved effects in a mathematical simple and effective model. This concerns the effects of Zeitgebers, feedback loops and further modifying components. We start from a known mathematical oscillator model, which is based on experimental molecular findings. This is extended with an effective framework that includes the impact of external stimuli on the circadian oscillations including high dose pharmacological treatment. In particular, the external stimuli framework defines a systematic procedure by input-output-interfaces to couple different oscillators. The framework is validated by providing phase response curves and ranges of entrainment. Furthermore, Aschoffs rule is computationally investigated. It is shown how the external stimuli framework can be used to study biological effects like points of singularity or oscillators integrating different signals at once. The mathematical framework and formalism is generic and allows to study in general the effect of external stimuli on oscillators and other biological processes. For an easy replication of each numerical experiment presented in this work and an easy implementation of the framework the corresponding Mathematica files are fully made available. They can be downloaded at the following link: https://www.biozentrum.uni-wuerzburg.de/bioinfo/computing/circadian/.
Clinical trials of novel therapeutics for Alzheimer’s Disease (AD) have consumed a significant amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA), European Medicines Agency (EMA), or Worldwide for another indication is a more rapid and less expensive option. Therefore, we apply the scaffold searching approach based on known amyloid-beta (Aβ) inhibitor tramiprosate to screen the DrugCentral database (n = 4,642) of clinically tested drugs. As a result, menadione bisulfite and camphotamide substances with protrombogenic and neurostimulation/cardioprotection effects were identified as promising Aβ inhibitors with an improved binding affinity (ΔGbind) and blood-brain barrier permeation (logBB). Finally, the data was also confirmed by molecular dynamics simulations using implicit solvation, in particular as Molecular Mechanics Generalized Born Surface Area (MM-GBSA) model. Overall, the proposed in silico pipeline can be implemented through the early stage rational drug design to nominate some lead candidates for AD, which will be further validated in vitro and in vivo, and, finally, in a clinical trial.
Aspergillus is an important fungal genus containing economically important species, as well as pathogenic species of animals and plants. Using eighteen fungal species of the genus Aspergillus, we conducted a comprehensive investigation of conserved genes and their evolution. This also allows us to investigate the selection pressure driving the adaptive evolution in the pathogenic species A. fumigatus. Among single-copy orthologs (SCOs) for A. fumigatus and the closely related species A. fischeri, we identified 122 versus 50 positively selected genes (PSGs), respectively. Moreover, twenty conserved genes of unknown function were established to be positively selected and thus important for adaption. A. fumigatus PSGs interacting with human host proteins show over-representation of adaptive, symbiosis-related, immunomodulatory and virulence-related pathways, such as the TGF-β pathway, insulin receptor signaling, IL1 pathway and interfering with phagosomal GTPase signaling. Additionally, among the virulence factor coding genes, secretory and membrane protein-coding genes in multi-copy gene families, 212 genes underwent positive selection and also suggest increased adaptation, such as fungal immune evasion mechanisms (aspf2), siderophore biosynthesis (sidD), fumarylalanine production (sidE), stress tolerance (atfA) and thermotolerance (sodA). These genes presumably contribute to host adaptation strategies. Genes for the biosynthesis of gliotoxin are shared among all the close relatives of A. fumigatus as an ancient defense mechanism. Positive selection plays a crucial role in the adaptive evolution of A. fumigatus. The genome-wide profile of PSGs provides valuable targets for further research on the mechanisms of immune evasion, antimycotic targeting and understanding fundamental virulence processes.
The olive tree is a venerable Mediterranean plant and often used in traditional medicine. The main aim of the present study was to evaluate the effect of Olea europaea L. cv. Arbosana leaf extract (OLE) and its encapsulation within a spanlastic dosage form on the improvement of its pro-oxidant and antiproliferative activity against HepG-2, MCF-7, and Caco-2 human cancer cell lines. The LC-HRESIMS-assisted metabolomic profile of OLE putatively annotated 20 major metabolites and showed considerable in vitro antiproliferative activity against HepG-2, MCF-7, and Caco-2 cell lines with IC\(_{50}\) values of 9.2 ± 0.8, 7.1 ± 0.9, and 6.5 ± 0.7 µg/mL, respectively. The encapsulation of OLE within a (spanlastic) nanocarrier system, using a spraying method and Span 40 and Tween 80 (4:1 molar ratio), was successfully carried out (size 41 ± 2.4 nm, zeta potential 13.6 ± 2.5, and EE 61.43 ± 2.03%). OLE showed enhanced thermal stability, and an improved in vitro antiproliferative effect against HepG-2, MCF-7, and Caco-2 (IC\(_{50}\) 3.6 ± 0.2, 2.3 ± 0.1, and 1.8 ± 0.1 µg/mL, respectively) in comparison to the unprocessed extract. Both preparations were found to exhibit pro-oxidant potential inside the cancer cells, through the potential inhibitory activity of OLE against glutathione reductase and superoxide dismutase (IC\(_{50}\) 1.18 ± 0.12 and 2.33 ± 0.19 µg/mL, respectively). These inhibitory activities were proposed via a comprehensive in silico study to be linked to the presence of certain compounds in OLE. Consequently, we assume that formulating such a herbal extract within a suitable nanocarrier would be a promising improvement of its therapeutic potential.
We explore a cosmology where the Big Bang singularity is replaced by a condensation event of interacting strings. We study the transition from an uncontrolled, chaotic soup (“before”) to a clearly interacting “real world”. Cosmological inflation scenarios do not fit current observations and are avoided. Instead, long-range interactions inside this crystallization event limit growth and crystal symmetries ensure the same laws of nature and basic symmetries over our domain. Tiny mis-arrangements present nuclei of superclusters and galaxies and crystal structure leads to the arrangement of dark (halo regions) and normal matter (galaxy nuclei) so convenient for galaxy formation. Crystals come and go, allowing an evolutionary cosmology where entropic forces from the quantum soup “outside” of the crystal try to dissolve it. These would correspond to dark energy and leads to a big rip scenario in 70 Gy. Preference of crystals with optimal growth and most condensation nuclei for the next generation of crystals may select for multiple self-organizing processes within the crystal, explaining “fine-tuning” of the local “laws of nature” (the symmetry relations formed within the crystal, its “unit cell”) to be particular favorable for self-organizing processes including life or even conscious observers in our universe.
Independent of cosmology, a crystallization event may explain quantum-decoherence in general: The fact, that in our macroscopic everyday world we only see one reality. This contrasts strongly with the quantum world where you have coherence, a superposition of all quantum states. We suggest that a “real world” (so our everyday macroscopic world) happens only in our domain, i.e. inside a crystal. “Outside” of our domain and our observable universe there is the quantum soup of boiling quantum foam and superposition of all possibilities. In our crystallized world the vacuum no longer boils but is cooled down by the crystallization event and hence is 10**20 smaller, exactly as observed in our everyday world. As we live in a “solid” state, within a crystal, the different quanta which build our world have all their different states nicely separated. This theory postulates there are only n quanta and m states available for them (there is no Everett-like ever splitting multiverse after each decision). In the solid state we live in, there is decoherence, the states are nicely separated. The arrow of entropy for each edge of the crystal forms one fate, one worldline or clear development of a world, while the layers of the crystal are different system states.
Some mathematical leads from loop quantum gravity point to required interactions and potentials. A complete mathematical treatment of this unified theory is far too demanding currently. Interaction potentials for strings or membranes of any dimension allow a solid state of quanta, so allowing decoherence in our observed world are challenging to calculate. However, if we introduce here the heuristic that any type of physical interaction of strings corresponds just to a type of calculation, there is already since 1898 the Hurwitz theorem showing that then only 1D, 2D, 4D and 8D (octonions) allow complex or hypercomplex number calculations. No other hypercomplex numbers and hence dimensions or symmetries are possible to allow calculations without yielding divisions by zero. However, the richest solution allowed by the Hurwitz theorem, octonions, is actually the observed symmetry of our universe, E8.
High attrition-rates entailed by drug testing in 2D cell culture and animal models stress the need for improved modeling of human tumor tissues. In previous studies our 3D models on a decellularized tissue matrix have shown better predictivity and higher chemoresistance. A single porcine intestine yields material for 150 3D models of breast, lung, colorectal cancer (CRC) or leukemia. The uniquely preserved structure of the basement membrane enables physiological anchorage of endothelial cells and epithelial-derived carcinoma cells. The matrix provides different niches for cell growth: on top as monolayer, in crypts as aggregates and within deeper layers. Dynamic culture in bioreactors enhances cell growth. Comparing gene expression between 2D and 3D cultures, we observed changes related to proliferation, apoptosis and stemness. For drug target predictions, we utilize tumor-specific sequencing data in our in silico model finding an additive effect of metformin and gefitinib treatment for lung cancer in silico, validated in vitro. To analyze mode-of-action, immune therapies such as trispecific T-cell engagers in leukemia, as well as toxicity on non-cancer cells, the model can be modularly enriched with human endothelial cells (hECs), immune cells and fibroblasts. Upon addition of hECs, transmigration of immune cells through the endothelial barrier can be investigated. In an allogenic CRC model we observe a lower basic apoptosis rate after applying PBMCs in 3D compared to 2D, which offers new options to mirror antigen-specific immunotherapies in vitro. In conclusion, we present modular human 3D tumor models with tissue-like features for preclinical testing to reduce animal experiments.
To enable a sustainable supply of chemicals, novel biotechnological solutions are required that replace the reliance on fossil resources. One potential solution is to utilize tailored biosynthetic modules for the metabolic conversion of CO2 or organic waste to chemicals and fuel by microorganisms. Currently, it is challenging to commercialize biotechnological processes for renewable chemical biomanufacturing because of a lack of highly active and specific biocatalysts. As experimental methods to engineer biocatalysts are time- and cost-intensive, it is important to establish efficient and reliable computational tools that can speed up the identification or optimization of selective, highly active, and stable enzyme variants for utilization in the biotechnological industry. Here, we review and suggest combinations of effective state-of-the-art software and online tools available for computational enzyme engineering pipelines to optimize metabolic pathways for the biosynthesis of renewable chemicals. Using examples relevant for biotechnology, we explain the underlying principles of enzyme engineering and design and illuminate future directions for automated optimization of biocatalysts for the assembly of synthetic metabolic pathways.
Synthetically designed alternative photorespiratory pathways increase the biomass of tobacco and rice plants. Likewise, some in planta–tested synthetic carbon-concentrating cycles (CCCs) hold promise to increase plant biomass while diminishing atmospheric carbon dioxide burden. Taking these individual contributions into account, we hypothesize that the integration of bypasses and CCCs will further increase plant productivity. To test this in silico, we reconstructed a metabolic model by integrating photorespiration and photosynthesis with the synthetically designed alternative pathway 3 (AP3) enzymes and transporters. We calculated fluxes of the native plant system and those of AP3 combined with the inhibition of the glycolate/glycerate transporter by using the YANAsquare package. The activity values corresponding to each enzyme in photosynthesis, photorespiration, and for synthetically designed alternative pathways were estimated. Next, we modeled the effect of the crotonyl-CoA/ethylmalonyl-CoA/hydroxybutyryl-CoA cycle (CETCH), which is a set of natural and synthetically designed enzymes that fix CO₂ manifold more than the native Calvin–Benson–Bassham (CBB) cycle. We compared estimated fluxes across various pathways in the native model and under an introduced CETCH cycle. Moreover, we combined CETCH and AP3-w/plgg1RNAi, and calculated the fluxes. We anticipate higher carbon dioxide–harvesting potential in plants with an AP3 bypass and CETCH–AP3 combination. We discuss the in vivo implementation of these strategies for the improvement of C3 plants and in natural high carbon harvesters.
The rapid development of green and sustainable materials opens up new possibilities in the field of applied research. Such materials include nanocellulose composites that can integrate many components into composites and provide a good chassis for smart devices. In our study, we evaluate four approaches for turning a nanocellulose composite into an information storage or processing device: 1) nanocellulose can be a suitable carrier material and protect information stored in DNA. 2) Nucleotide-processing enzymes (polymerase and exonuclease) can be controlled by light after fusing them with light-gating domains; nucleotide substrate specificity can be changed by mutation or pH change (read-in and read-out of the information). 3) Semiconductors and electronic capabilities can be achieved: we show that nanocellulose is rendered electronic by iodine treatment replacing silicon including microstructures. Nanocellulose semiconductor properties are measured, and the resulting potential including single-electron transistors (SET) and their properties are modeled. Electric current can also be transported by DNA through G-quadruplex DNA molecules; these as well as classical silicon semiconductors can easily be integrated into the nanocellulose composite. 4) To elaborate upon miniaturization and integration for a smart nanocellulose chip device, we demonstrate pH-sensitive dyes in nanocellulose, nanopore creation, and kinase micropatterning on bacterial membranes as well as digital PCR micro-wells. Future application potential includes nano-3D printing and fast molecular processors (e.g., SETs) integrated with DNA storage and conventional electronics. This would also lead to environment-friendly nanocellulose chips for information processing as well as smart nanocellulose composites for biomedical applications and nano-factories.
Since ancient times aging has also been regarded as a disease, and humankind has always strived to extend the natural lifespan. Analyzing the genes involved in aging and disease allows for finding important indicators and biological markers for pathologies and possible therapeutic targets. An example of the use of omics technologies is the research regarding aging and the rare and fatal premature aging syndrome progeria (Hutchinson-Gilford progeria syndrome, HGPS). In our study, we focused on the in silico analysis of differentially expressed genes (DEGs) in progeria and aging, using a publicly available RNA-Seq dataset (GEO dataset GSE113957) and a variety of bioinformatics tools. Despite the GSE113957 RNA-Seq dataset being well-known and frequently analyzed, the RNA-Seq data shared by Fleischer et al. is far from exhausted and reusing and repurposing the data still reveals new insights. By analyzing the literature citing the use of the dataset and subsequently conducting a comparative analysis comparing the RNA-Seq data analyses of different subsets of the dataset (healthy children, nonagenarians and progeria patients), we identified several genes involved in both natural aging and progeria (KRT8, KRT18, ACKR4, CCL2, UCP2, ADAMTS15, ACTN4P1, WNT16, IGFBP2). Further analyzing these genes and the pathways involved indicated their possible roles in aging, suggesting the need for further in vitro and in vivo research. In this paper, we (1) compare “normal aging” (nonagenarians vs. healthy children) and progeria (HGPS patients vs. healthy children), (2) enlist genes possibly involved in both the natural aging process and progeria, including the first mention of IGFBP2 in progeria, (3) predict miRNAs and interactomes for WNT16 (hsa-mir-181a-5p), UCP2 (hsa-mir-26a-5p and hsa-mir-124-3p), and IGFBP2 (hsa-mir-124-3p, hsa-mir-126-3p, and hsa-mir-27b-3p), (4) demonstrate the compatibility of well-established R packages for RNA-Seq analysis for researchers interested but not yet familiar with this kind of analysis, and (5) present comparative proteomics analyses to show an association between our RNA-Seq data analyses and corresponding changes in protein expression.
Nature is a rich source of biologically active novel compounds. Sixty years ago, the plant hormones cytokinins were first discovered. These play a major role in cell division and cell differentiation. They affect organogenesis in plant tissue cultures and contribute to many other physiological and developmental processes in plants. Consequently, the effect of cytokinins on mammalian cells has caught the attention of researchers. Many reports on the contribution and potential of cytokinins in the therapy of different human diseases and pathophysiological conditions have been published and are reviewed here. We compare cytokinin effects and pathways in plants and mammalian systems and highlight the most important biological activities. We present the strong profile of the biological actions of cytokinins and their possible therapeutic applications.
Epithelial-to-mesenchymal transition (EMT) is discussed to be centrally involved in invasion, stemness, and drug resistance. Experimental models to evaluate this process in its biological complexity are limited. To shed light on EMT impact and test drug response more reliably, we use a lung tumor test system based on a decellularized intestinal matrix showing more in vivo-like proliferation levels and enhanced expression of clinical markers and carcinogenesis-related genes. In our models, we found evidence for a correlation of EMT with drug resistance in primary and secondary resistant cells harboring KRAS\(^{G12C}\) or EGFR mutations, which was simulated in silico based on an optimized signaling network topology. Notably, drug resistance did not correlate with EMT status in KRAS-mutated patient-derived xenograft (PDX) cell lines, and drug efficacy was not affected by EMT induction via TGF-β. To investigate further determinants of drug response, we tested several drugs in combination with a KRAS\(^{G12C}\) inhibitor in KRAS\(^{G12C}\) mutant HCC44 models, which, besides EMT, display mutations in P53, LKB1, KEAP1, and high c-MYC expression. We identified an aurora-kinase A (AURKA) inhibitor as the most promising candidate. In our network, AURKA is a centrally linked hub to EMT, proliferation, apoptosis, LKB1, and c-MYC. This exemplifies our systemic analysis approach for clinical translation of biomarker signatures.
An epidemic of avian type H7N9 influenza virus, which took place in China in 2013, was enhanced by a naturally occurring R294K mutation resistant against Oseltamivir at the catalytic site of the neuraminidase. To cope with such drug-resistant neuraminidase mutations, we applied the molecular docking technique to evaluate the fitness of the available drugs such as Oseltamivir, Zanamivir, Peramivir, Laninamivir, L-Arginine and Benserazide hydrochloride concerning the N9 enzyme with single (R294K, R119K, R372K), double (R119_294K, R119_372K, R294_372K) and triple (R119_294_372K) mutations in the pocket. We found that the drugs Peramivir and Zanamivir score best amongst the studied compounds, demonstrating their high binding potential towards the pockets with the considered mutations. Despite the fact that mutations changed the shape of the pocket and reduced the binding strength for all drugs, Peramivir was the only drug that formed interactions with the key residues at positions 119, 294 and 372 in the pocket of the triple N9 mutant, while Zanamivir demonstrated the lowest RMSD value (0.7 Å) with respect to the reference structure.
After the recent emergence of SARS-CoV-2 infection, unanswered questions remain related to its evolutionary history, path of transmission or divergence and role of recombination. There is emerging evidence on amino acid substitutions occurring in key residues of the receptor-binding domain of the spike glycoprotein in coronavirus isolates from bat and pangolins. In this article, we summarize our current knowledge on the origin of SARS-CoV-2. We also analyze the host ACE2-interacting residues of the receptor-binding domain of spike glycoprotein in SARS-CoV-2 isolates from bats, and compare it to pangolin SARS-CoV-2 isolates collected from Guangdong province (GD Pangolin-CoV) and Guangxi autonomous regions (GX Pangolin-CoV) of South China. Based on our comparative analysis, we support the view that the Guangdong Pangolins are the intermediate hosts that adapted the SARS-CoV-2 and represented a significant evolutionary link in the path of transmission of SARS-CoV-2 virus. We also discuss the role of intermediate hosts in the origin of Omicron.
The variable regions (V1–V9) of the 18S rDNA are routinely used in barcoding and phylogenetics. In handling these data for trypanosomes, we have noticed a misunderstanding that has apparently taken a life of its own in the literature over the years. In particular, in recent years, when studying the phylogenetic relationship of trypanosomes, the use of V7/V8 was systematically established. However, considering the current numbering system for all other organisms (including other Euglenozoa), V7/V8 was never used. In Maia da Silva et al. [Parasitology 2004, 129, 549–561], V7/V8 was promoted for the first time for trypanosome phylogenetics, and since then, more than 70 publications have replicated this nomenclature and even discussed the benefits of the use of this region in comparison to V4. However, the primers used to amplify the variable region of trypanosomes have actually amplified V4 (concerning the current 18S rDNA numbering system).
Nucleic acid motifs consist of conserved and variable nucleotide regions. For functional action, several motifs are combined to modules. The tool AIModules allows identification of such motifs including combinations of them and conservation in several nucleic acid stretches. AIModules recognizes conserved motifs and combinations of motifs (modules) allowing a number of interesting biological applications such as analysis of promoter and transcription factor binding sites (TFBS), identification of conserved modules shared between several gene families, e.g. promoter regions, but also analysis of shared and conserved other DNA motifs such as enhancers and silencers, in mRNA (motifs or regulatory elements e.g. for polyadenylation) and lncRNAs. The tool AIModules presented here is an integrated solution for motif analysis, offered as a Web service as well as downloadable software. Several nucleotide sequences are queried for TFBSs using predefined matrices from the JASPAR DB or by using one’s own matrices for diverse types of DNA or RNA motif discovery. Furthermore, AIModules can find TFBSs common to two or more sequences. Demanding high or low conservation, AIModules outperforms other solutions in speed and finds more modules (specific combinations of TFBS) than alternative available software. The application also searches RNA motifs such as polyadenylation site or RNA–protein binding motifs as well as DNA motifs such as enhancers as well as user-specified motif combinations (https://bioinfo-wuerz.de/aimodules/; alternative entry pages: https://aimodules.heinzelab.de or https://www.biozentrum.uni-wuerzburg.de/bioinfo/computing/aimodules). The application is free and open source whether used online, on-site, or locally.
Cisplatin is a commonly used chemotherapeutic agent; however, its potential side effects, including gonadotoxicity and infertility, are a critical problem. Oxidative stress has been implicated in the pathogenesis of cisplatin-induced testicular dysfunction. We investigated whether kinetin use at different concentrations could alleviate gonadal injury associated with cisplatin treatment, with an exploration of the involvement of its antioxidant capacity. Kinetin was administered in different doses of 0.25, 0.5, and 1 mg/kg, alone or along with cisplatin for 10 days. Cisplatin toxicity was induced via a single IP dose of 7 mg/kg on day four. In a dose-dependent manner, concomitant administration of kinetin with cisplatin significantly restored testicular oxidative stress parameters, corrected the distorted sperm quality parameters and histopathological changes, enhanced levels of serum testosterone and testicular StAR protein expression, as well as reduced the up-regulation of testicular TNF-α, IL-1β, Il-6, and caspase-3, caused by cisplatin. It is worth noting that the testicular protective effect of the highest kinetin dose was comparable/more potent and significantly higher than the effects of vitamin C and the lowest kinetin dose, respectively. Overall, these data indicate that kinetin may offer a promising approach for alleviating cisplatin-induced reproductive toxicity and organ damage, via ameliorating oxidative stress and reducing inflammation and apoptosis.
In this view point we do not change cosmology after the hot fireball starts (hence agrees well with observation), but the changed start suggested and resulting later implications lead to an even better fit with current observations (voids, supercluster and galaxy formation; matter and no antimatter) than the standard model with big bang and inflation: In an eternal ocean of qubits, a cluster of qubits crystallizes to defined bits. The universe does not jump into existence (“big bang”) but rather you have an eternal ocean of qubits in free super-position of all their quantum states (of any dimension, force field and particle type) as permanent basis. The undefined, boiling vacuum is the real “outside”, once you leave our everyday universe. A set of n Qubits in the ocean are “liquid”, in very undefined state, they have all their m possibilities for quantum states in free superposition. However, under certain conditions the qubits interact, become defined, and freeze out, crystals form and give rise to a defined, real world with all possible time series and world lines. GR holds only within the crystal. In our universe all n**m quantum possibilities are nicely separated and crystallized out to defined bit states: A toy example with 6 qubits each having 2 states illustrates, this is completely sufficient to encode space using 3 bits for x,y and z, 1 bit for particle type and 2 bits for its state. Just by crystallization, space, particles and their properties emerge from the ocean of qubits, and following the arrow of entropy, time emerges, following an arrow of time and expansion from one corner of the toy universe to everywhere else. This perspective provides time as emergent feature considering entropy: crystallization of each world line leads to defined world lines over their whole existence, while entropy ensures direction of time and higher representation of high entropy states considering the whole crystal and all slices of world lines. The crystal perspective is also economic compared to the Everett-type multiverse, each qubit has its m quantum states and n qubits interacting forming a crystal and hence turning into defined bit states has only n**m states and not more states. There is no Everett-type world splitting with every decision but rather individual world trajectories reside in individual world layers of the crystal. Finally, bit-separated crystals come and go in the qubit ocean, selecting for the ability to lay seeds for new crystals. This self-organizing reproduction selects over generations also for life-friendliness. Mathematical treatment introduces quantum action theory as a framework for a general lattice field theory extending quantum chromo dynamics where scalar fields for color interaction and gravity have to be derived from the permeating qubit-interaction field. Vacuum energy should get appropriately low by the binding properties of the qubit crystal. Connections to loop quantum gravity, string theory and emergent gravity are discussed. Standard physics (quantum computing; crystallization, solid state physics) allow validation tests of this perspective and will extend current results.
Though several previous studies reported the in vitro and in vivo antioxidant effect of kinetin (Kn), details on its action in cisplatin-induced toxicity are still scarce. In this study we evaluated, for the first time, the effects of kinetin in cisplatin (cp)- induced liver and lymphocyte toxicity in rats. Wistar male albino rats were divided into nine groups: (i) the control (C), (ii) groups 2,3 and 4, which received 0.25, 0.5 and 1 mg/kg kinetin for 10 days; (iii) the cisplatin (cp) group, which received a single intraperitoneal injection of CP (7.0 mg/kg); and (iv) groups 6, 7, 8 and 9, which received, for 10 days, 0.25, 0.5 and 1 mg/kg kinetin or 200 mg/kg vitamin C, respectively, and Cp on the fourth day. CP-injected rats showed a significant impairment in biochemical, oxidative stress and inflammatory parameters in hepatic tissue and lymphocytes. PCR showed a profound increase in caspase-3, and a significant decline in AKT gene expression. Intriguingly, Kn treatment restored the biochemical, redox status and inflammatory parameters. Hepatic AKT and caspase-3 expression as well as CD95 levels in lymphocytes were also restored. In conclusion, Kn mitigated oxidative imbalance, inflammation and apoptosis in CP-induced liver and lymphocyte toxicity; therefore, it can be considered as a promising therapy.
Candida auris is a globally emerging fungal pathogen responsible for causing nosocomial outbreaks in healthcare associated settings. It is known to cause infection in all age groups and exhibits multi-drug resistance with high potential for horizontal transmission. Because of this reason combined with limited therapeutic choices available, C. auris infection has been acknowledged as a potential risk for causing a future pandemic, and thus seeking a promising strategy for its treatment is imperative. Here, we combined evolutionary information with reverse vaccinology approach to identify novel epitopes for vaccine design that could elicit CD4+ T-cell responses against C. auris. To this end, we extensively scanned the family of proteins encoded by C. auris genome. In addition, a pathogen may acquire substitutions in epitopes over a period of time which could cause its escape from the immune response thus rendering the vaccine ineffective. To lower this possibility in our design, we eliminated all rapidly evolving genes of C. auris with positive selection. We further employed highly conserved regions of multiple C. auris strains and identified two immunogenic and antigenic T-cell epitopes that could generate the most effective immune response against C. auris. The antigenicity scores of our predicted vaccine candidates were calculated as 0.85 and 1.88 where 0.5 is the threshold for prediction of fungal antigenic sequences. Based on our results, we conclude that our vaccine candidates have the potential to be successfully employed for the treatment of C. auris infection. However, in vivo experiments are imperative to further demonstrate the efficacy of our design.
For SARS-CoV-2, R0 calculations in the range of 2–3 dominate the literature, but much higher estimates have also been published. Because capacity for RT-PCR testing increased greatly in the early phase of the Covid-19 pandemic, R0 determinations based on these incidence values are subject to strong bias. We propose to use Covid-19-induced excess mortality to determine R0 regardless of RT-PCR testing capacity. We used data from the Robert Koch Institute (RKI) on the incidence of Covid cases, Covid-related deaths, number of RT-PCR tests performed, and excess mortality calculated from data from the Federal Statistical Office in Germany. We determined R0 using exponential growth estimates with a serial interval of 4.7 days. We used only datasets that were not yet under the influence of policy measures (e.g., lockdowns or school closures). The uncorrected R0 value for the spread of SARS-CoV-2 based on RT-PCR incidence data was 2.56 (95% CI 2.52–2.60) for Covid-19 cases and 2.03 (95% CI 1.96–2.10) for Covid-19-related deaths. However, because the number of RT-PCR tests increased by a growth factor of 1.381 during the same period, these R0 values must be corrected accordingly (R0corrected = R0uncorrected/1.381), yielding 1.86 for Covid-19 cases and 1.47 for Covid-19 deaths. The R0 value based on excess deaths was calculated to be 1.34 (95% CI 1.32–1.37). A sine-function-based adjustment for seasonal effects of 40% corresponds to a maximum value of R0January = 1.68 and a minimum value of R0July = 1.01. Our calculations show an R0 that is much lower than previously thought. This relatively low range of R0 fits very well with the observed seasonal pattern of infection across Europe in 2020 and 2021, including the emergence of more contagious escape variants such as delta or omicron. In general, our study shows that excess mortality can be used as a reliable surrogate to determine the R0 in pandemic situations.
In a modified inflation scenario we replace the “big bang” by a condensation event in an eternal all-compassing big ocean of free qubits in our modified cosmology. Interactions of qubits in the qubit ocean are rare. If they happen, they provide a nucleus for a new universe as the qubits become decoherent and freeze-out into defined bit ensembles. Second, we replace inflation by a crystallization event triggered by the nucleus of interacting qubits to which rapidly more and more qubits attach (like in everyday crystal growth) – the crystal unit cell guarantees same symmetries everywhere. Hence, the textbook inflation scenario to explain the same laws of nature in our domain is replaced by the crystal unit cell of the crystal formed. We give here only the perspective or outline of this modified inflation theory, as the detailed mathematical physics behind this has still to be formulated and described.
Interacting qubits solidify, quantum entropy decreases (but increases in the ocean around). The interacting qubits form a rapidly growing domain where the n**m states become separated ensemble states, rising long-range forces stop ultimately further growth. After that very early events, standard cosmology with the hot fireball model takes over. Our theory agrees well with lack of inflation traces in cosmic background measurements, but more importantly can explain well by such a type of cosmological crystallization instead of inflation the early creation of large-scale structure of voids and filaments, supercluster formation, galaxy formation, and the dominance of matter: no annihilation of antimatter necessary, rather the unit cell of our crystal universe has a matter handedness avoiding anti-matter.
We prove a triggering of qubit interactions can only be 1,2,4 or 8-dimensional (agrees with E8 symmetry of our universe). Repulsive forces at ultrashort distances result from quantization, long-range forces limit crystal growth. Crystals come and go in the qubit ocean. This selects for the ability to lay seeds for new crystals, for self-organization and life-friendliness.
The phase space of the crystal agrees with the standard model of the basic four forces for n quanta. It includes all possible ensemble combinations of their quantum states m, a total of n**m states. Neighbor states reach according to transition possibilities (S-matrix) with emergent time from entropic ensemble gradients. However, this means that in our four dimensions there is only one bit overlap to neighbor states left (almost solid, only below h dash liquidity left). However, the E8 symmetry of heterotic string theory has six rolled-up, small dimensions which help to keep the qubit crystal together and will never expand.
Finally, we give first energy estimates for free qubits vs bound qubits, misplacements in the qubit crystal and entropy increase during qubit decoherence / crystal formation. Scalar fields for color interaction and gravity derive from the permeating qubit-interaction field in the crystal. Hence, vacuum energy gets low inside the qubit crystal. Condensed mathematics may advantageously help to model free (many states denote the same qubit) and bound qubits in phase space.
The signal modelling framework JimenaE simulates dynamically Boolean networks. In contrast to SQUAD, there is systematic and not just heuristic calculation of all system states. These specific features are not present in CellNetAnalyzer and BoolNet. JimenaE is an expert extension of Jimena, with new optimized code, network conversion into different formats, rapid convergence both for system state calculation as well as for all three network centralities. It allows higher accuracy in determining network states and allows to dissect networks and identification of network control type and amount for each protein with high accuracy. Biological examples demonstrate this: (i) High plasticity of mesenchymal stromal cells for differentiation into chondrocytes, osteoblasts and adipocytes and differentiation-specific network control focusses on wnt-, TGF-beta and PPAR-gamma signaling. JimenaE allows to study individual proteins, removal or adding interactions (or autocrine loops) and accurately quantifies effects as well as number of system states. (ii) Dynamical modelling of cell–cell interactions of plant Arapidopsis thaliana against Pseudomonas syringae DC3000: We analyze for the first time the pathogen perspective and its interaction with the host. We next provide a detailed analysis on how plant hormonal regulation stimulates specific proteins and who and which protein has which type and amount of network control including a detailed heatmap of the A.thaliana response distinguishing between two states of the immune response. (iii) In an immune response network of dendritic cells confronted with Aspergillus fumigatus, JimenaE calculates now accurately the specific values for centralities and protein-specific network control including chemokine and pattern recognition receptors.
Glycoprotein VI (GPVI) is a platelet-specific receptor for collagen and fibrin, regulating important platelet functions such as platelet adhesion and thrombus growth. Although the blockade of GPVI function is widely recognized as a potent anti-thrombotic approach, there are limited studies focused on site-specific targeting of GPVI. Using computational modeling and bioinformatics, we analyzed collagen- and CRP-binding surfaces of GPVI monomers and dimers, and compared the interacting surfaces with other mammalian GPVI isoforms. We could predict a minimal collagen-binding epitope of GPVI dimer and designed an EA-20 antibody that recognizes a linear epitope of this surface. Using platelets and whole blood samples donated from wild-type and humanized GPVI transgenic mice and also humans, our experimental results show that the EA-20 antibody inhibits platelet adhesion and aggregation in response to collagen and CRP, but not to fibrin. The EA-20 antibody also prevents thrombus formation in whole blood, on the collagen-coated surface, in arterial flow conditions. We also show that EA-20 does not influence GPVI clustering or receptor shedding. Therefore, we propose that blockade of this minimal collagen-binding epitope of GPVI with the EA-20 antibody could represent a new anti-thrombotic approach by inhibiting specific interactions between GPVI and the collagen matrix.
Natural DNA storage allows cellular differentiation, evolution, the growth of our children and controls all our ecosystems. Here, we discuss the fundamental aspects of DNA storage and recent advances in this field, with special emphasis on natural processes and solutions that can be exploited. We point out new ways of efficient DNA and nucleotide storage that are inspired by nature. Within a few years DNA-based information storage may become an attractive and natural complementation to current electronic data storage systems. We discuss rapid and directed access (e.g. DNA elements such as promotors, enhancers), regulatory signals and modulation (e.g. lncRNA) as well as integrated high-density storage and processing modules (e.g. chromosomal territories). There is pragmatic DNA storage for use in biotechnology and human genetics. We examine DNA storage as an approach for synthetic biology (e.g. light-controlled nucleotide processing enzymes). The natural polymers of DNA and RNA offer much for direct storage operations (read-in, read-out, access control). The inbuilt parallelism (many molecules at many places working at the same time) is important for fast processing of information. Using biology concepts from chromosomal storage, nucleic acid processing as well as polymer material sciences such as electronical effects in enzymes, graphene, nanocellulose up to DNA macramé , DNA wires and DNA-based aptamer field effect transistors will open up new applications gradually replacing classical information storage methods in ever more areas over time (decades).
Introduction
Pro-thrombotic events are one of the prevalent causes of intensive care unit (ICU) admissions among COVID-19 patients, although the signaling events in the stimulated platelets are still unclear.
Methods
We conducted a comparative analysis of platelet transcriptome data from healthy donors, ICU, and non-ICU COVID-19 patients to elucidate these mechanisms. To surpass previous analyses, we constructed models of involved networks and control cascades by integrating a global human signaling network with transcriptome data. We investigated the control of platelet hyperactivation and the specific proteins involved.
Results
Our study revealed that control of the platelet network in ICU patients is significantly higher than in non-ICU patients. Non-ICU patients require control over fewer proteins for managing platelet hyperactivity compared to ICU patients. Identification of indispensable proteins highlighted key subnetworks, that are targetable for system control in COVID-19-related platelet hyperactivity. We scrutinized FDA-approved drugs targeting indispensable proteins and identified fostamatinib as a potent candidate for preventing thrombosis in COVID-19 patients.
Discussion
Our findings shed light on how SARS-CoV-2 efficiently affects host platelets by targeting indispensable and critical proteins involved in the control of platelet activity. We evaluated several drugs for specific control of platelet hyperactivity in ICU patients suffering from platelet hyperactivation. The focus of our approach is repurposing existing drugs for optimal control over the signaling network responsible for platelet hyperactivity in COVID-19 patients. Our study offers specific pharmacological recommendations, with drug prioritization tailored to the distinct network states observed in each patient condition. Interactive networks and detailed results can be accessed at https://fostamatinib.bioinfo-wuerz.eu/.
Protein folding achieves a clear solution structure in a huge parameter space (the so-called protein folding problem). Proteins fold in water, and get by this a highly ordered structure. Finally, inside a protein crystal for structure resolution, you have everywhere the same symmetries as there is everywhere the same unit cell. We apply this to qubit interactions to do fundamental physics:
in a modified cosmology, we replace the big bang by a condensation event in an eternal all-encompassing ocean of free qubits. Interactions of qubits in the qubit ocean are quite rare but provide a nucleus or seed for a new universe (domain) as the qubits become decoherent and freeze-out into defined bit ensembles. Second, we replace inflation by a crystallization event triggered by the nucleus of interacting qubits to which rapidly more and more qubits attach (like in everyday crystal growth). The crystal unit cell guarantees same symmetries everywhere inside the crystal. The textbook inflation scenario to explain the same laws of nature in our domain is replaced by the unit cell of the crystal formed.
Interacting qubits solidify, quantum entropy decreases (but increases in the ocean around). In a modified inflation scenario, the interacting qubits form a rapidly growing domain where the n**m states become separated ensemble states, rising long-range forces stop ultimately further growth. Then standard cosmology with the hot fireball model takes over. Our theory agrees well with lack of inflation traces in cosmic background measurements. We explain by cosmological crystallization instead of inflation: early creation of large-scale structure of voids and filaments, supercluster formation, galaxy formation, and the dominance of matter: the unit cell of our crystal universe has a matter handedness avoiding anti-matter.
We prove initiation of qubit interactions can only be 1,2,4 or 8-dimensional (agrees with E8 symmetry of our universe). Repulsive forces at ultrashort distances result from quantization, long-range forces limit crystal growth. Crystals come and go in the qubit ocean. This selects for the ability to lay seeds for new crystals, for self-organization and life-friendliness.
The phase space of the crystal agrees with the standard model of the basic four forces for n quanta. It includes all possible ensemble combinations of their quantum states m, a total of n**m states. Neighbor states reach according to transition possibilities (S-matrix) with emergent time from entropic ensemble gradients. However, in our four dimensions there is only one bit overlap to neighbor states left (almost solid, only below Planck quantum there is liquidity left). The E8 symmetry of heterotic string theory has six curled-up, small dimensions which help to keep the qubit crystal together and will never expand.
Mathematics focusses on the Hurwitz proof applied to qubit interaction, a toy model of qubit interaction and repulsive forces of qubits. Vacuum energy gets appropriate low inside the crystal. We give first energy estimates for free qubits vs bound qubits, misplacements in the qubit crystal and entropy increase during qubit decoherence / crystal formation. Scalar fields for color interaction/confinement and gravity are derived from the qubit-interaction field.
PRO-Simat is a simulation tool for analysing protein interaction networks, their dynamic change and pathway engineering. It provides GO enrichment, KEGG pathway analyses, and network visualisation from an integrated database of more than 8 million protein-protein interactions across 32 model organisms and the human proteome. We integrated dynamical network simulation using the Jimena framework, which quickly and efficiently simulates Boolean genetic regulatory networks. It enables simulation outputs with in-depth analysis of the type, strength, duration and pathway of the protein interactions on the website. Furthermore, the user can efficiently edit and analyse the effect of network modifications and engineering experiments. In case studies, applications of PRO-Simat are demonstrated: (i) understanding mutually exclusive differentiation pathways in Bacillus subtilis, (ii) making Vaccinia virus oncolytic by switching on its viral replication mainly in cancer cells and triggering cancer cell apoptosis and (iii) optogenetic control of nucleotide processing protein networks to operate DNA storage. Multilevel communication between components is critical for efficient network switching, as demonstrated by a general census on prokaryotic and eukaryotic networks and comparing design with synthetic networks using PRO-Simat. The tool is available at https://prosimat.heinzelab.de/ as a web-based query server.
Machine learning techniques are excellent to analyze expression data from single cells. These techniques impact all fields ranging from cell annotation and clustering to signature identification. The presented framework evaluates gene selection sets how far they optimally separate defined phenotypes or cell groups. This innovation overcomes the present limitation to objectively and correctly identify a small gene set of high information content regarding separating phenotypes for which corresponding code scripts are provided. The small but meaningful subset of the original genes (or feature space) facilitates human interpretability of the differences of the phenotypes including those found by machine learning results and may even turn correlations between genes and phenotypes into a causal explanation. For the feature selection task, the principal feature analysis is utilized which reduces redundant information while selecting genes that carry the information for separating the phenotypes. In this context, the presented framework shows explainability of unsupervised learning as it reveals cell-type specific signatures. Apart from a Seurat preprocessing tool and the PFA script, the pipeline uses mutual information to balance accuracy and size of the gene set if desired. A validation part to evaluate the gene selection for their information content regarding the separation of the phenotypes is provided as well, binary and multiclass classification of 3 or 4 groups are studied. Results from different single-cell data are presented. In each, only about ten out of more than 30000 genes are identified as carrying the relevant information. The code is provided in a GitHub repository at https://github.com/AC-PHD/Seurat_PFA_pipeline.
In the fast-evolving landscape of biomedical research, the emergence of big data has presented researchers with extraordinary opportunities to explore biological complexities. In biomedical research, big data imply also a big responsibility. This is not only due to genomics data being sensitive information but also due to genomics data being shared and re-analysed among the scientific community. This saves valuable resources and can even help to find new insights in silico. To fully use these opportunities, detailed and correct metadata are imperative. This includes not only the availability of metadata but also their correctness. Metadata integrity serves as a fundamental determinant of research credibility, supporting the reliability and reproducibility of data-driven findings. Ensuring metadata availability, curation, and accuracy are therefore essential for bioinformatic research. Not only must metadata be readily available, but they must also be meticulously curated and ideally error-free. Motivated by an accidental discovery of a critical metadata error in patient data published in two high-impact journals, we aim to raise awareness for the need of correct, complete, and curated metadata. We describe how the metadata error was found, addressed, and present examples for metadata-related challenges in omics research, along with supporting measures, including tools for checking metadata and software to facilitate various steps from data analysis to published research.
Highlights
• Data awareness and data integrity underpins the trustworthiness of results and subsequent further analysis.
• Big data and bioinformatics enable efficient resource use by repurposing publicly available RNA-Sequencing data.
• Manual checks of data quality and integrity are insufficient due to the overwhelming volume and rapidly growing data.
• Automation and artificial intelligence provide cost-effective and efficient solutions for data integrity and quality checks.
• FAIR data management, various software solutions and analysis tools assist metadata maintenance.
Fungal infections are a major global health burden where Candida albicans is among the most common fungal pathogen in humans and is a common cause of invasive candidiasis. Fungal phenotypes, such as those related to morphology, proliferation and virulence are mainly driven by gene expression, which is primarily regulated by kinase signaling cascades. Serine-arginine (SR) protein kinases are highly conserved among eukaryotes and are involved in major transcriptional processes in human and S. cerevisiae. Candida albicans harbors two SR protein kinases, while Sky2 is important for metabolic adaptation, Sky1 has similar functions as in S. cerevisiae. To investigate the role of these SR kinases for the regulation of transcriptional responses in C. albicans, we performed RNA sequencing of sky1Δ and sky2Δ and integrated a comprehensive phosphoproteome dataset of these mutants. Using a Systems Biology approach, we study transcriptional regulation in the context of kinase signaling networks. Transcriptomic enrichment analysis indicates that pathways involved in the regulation of gene expression are downregulated and mitochondrial processes are upregulated in sky1Δ. In sky2Δ, primarily metabolic processes are affected, especially for arginine, and we observed that arginine-induced hyphae formation is impaired in sky2Δ. In addition, our analysis identifies several transcription factors as potential drivers of the transcriptional response. Among these, a core set is shared between both kinase knockouts, but it appears to regulate different subsets of target genes. To elucidate these diverse regulatory patterns, we created network modules by integrating the data of site-specific protein phosphorylation and gene expression with kinase-substrate predictions and protein-protein interactions. These integrated signaling modules reveal shared parts but also highlight specific patterns characteristic for each kinase. Interestingly, the modules contain many proteins involved in fungal morphogenesis and stress response. Accordingly, experimental phenotyping shows a higher resistance to Hygromycin B for sky1Δ. Thus, our study demonstrates that a combination of computational approaches with integration of experimental data can offer a new systems biological perspective on the complex network of signaling and transcription. With that, the investigation of the interface between signaling and transcriptional regulation in C. albicans provides a deeper insight into how cellular mechanisms can shape the phenotype.
The phase space for the standard model of the basic four forces for n quanta includes all possible ensemble combinations of their quantum states m, a total of n**m states. Neighbor states reach according to transition possibilities (S-matrix) with emergent time from entropic ensemble gradients.
We replace the “big bang” by a condensation event (interacting qubits become decoherent) and inflation by a crystallization event – the crystal unit cell guarantees same symmetries everywhere. Interacting qubits solidify and form a rapidly growing domain where the n**m states become separated ensemble states, rising long-range forces stop ultimately further growth. After that very early events, standard cosmology with the hot fireball model takes over. Our theory agrees well with lack of inflation traces in cosmic background measurements, large-scale structure of voids and filaments, supercluster formation, galaxy formation, dominance of matter and life-friendliness.
We prove qubit interactions to be 1,2,4 or 8 dimensional (agrees with E8 symmetry of our universe). Repulsive forces at ultrashort distances result from quantization, long-range forces limit crystal growth. Crystals come and go in the qubit ocean. This selects for the ability to lay seeds for new crystals, for self-organization and life-friendliness.
We give energy estimates for free qubits vs bound qubits, misplacements in the qubit crystal and entropy increase during qubit decoherence / crystal formation. Scalar fields for color interaction and gravity derive from the permeating qubit-interaction field. Hence, vacuum energy gets low only inside the qubit crystal. Condensed mathematics may advantageously model free / bound qubits in phase space.
Proteins fold in water and achieve a clear structure despite a huge parameter space. Inside a (protein) crystal you have everywhere the same symmetries as there is everywhere the same unit cell. We apply this to qubit interactions to do fundamental physics:
We modify cosmological inflation: we replace the big bang by a condensation event in an eternal all-encompassing ocean of free qubits. Rare interactions of qubits in the ocean provide a nucleus or seed for a new universe (domain), as the qubits become decoherent and freeze-out into defined bit ensembles. Next, we replace inflation by a crystallization event triggered by the nucleus of interacting qubits to which rapidly more and more qubits attach (like in everyday crystal growth). The crystal unit cell guarantees same symmetries (and laws of nature) everywhere inside the crystal, no inflation scenario is needed.
Interacting qubits solidify, quantum entropy decreases in the crystal, but increases outside in the ocean. The interacting qubits form a rapidly growing domain where the n**m states become separated ensemble states, rising long-range forces stop ultimately further growth. After this very early modified steps, standard cosmology with the hot fireball model takes over. Our theory agrees well with lack of inflation traces in cosmic background measurements.
Applying the Hurwitz theorem to qubits we prove that initiation of qubit interactions can only be 1,2,4 or 8-dimensional (agrees with E8 symmetry of our universe). Repulsive forces at ultrashort distances result from quantization, long-range forces limit crystal growth. The phase space of the crystal agrees with the standard model of the basic four forces for n quanta. It includes all possible ensemble combinations of their quantum states m, a total of n**m states. We describe a six-bit-ensemble toy model of qubit interaction and the repulsive forces of qubits for ultra-short distances. Neighbor states reach according to transition possibilities (S-matrix) with emergent time from entropic ensemble gradients. However, in our four dimensions there is only one bit overlap to neighbor states left (almost solid, only below Planck´s quantum is liquidity left). The E8 symmetry of heterotic string theory has six curled-up, small dimensions. These keep the qubit crystal together and never expand. We give energy estimates for free qubits vs bound qubits, misplacements in the qubit crystal and entropy increase during qubit crystal formation.
Implications are fundamental answers, e.g. why there is fine-tuning for life-friendliness, why there is string theory with rolled-up dimension and so many free parameters. We explain by cosmological crystallization instead of inflation the early creation of large-scale structure of voids and filaments, supercluster formation, galaxy formation, and the dominance of matter: the unit cell of our crystal universe has a matter handedness avoiding anti-matter. Importantly, crystals come and go in the qubit ocean. This selects for the ability to lay seeds for new crystals, for self-organization and life-friendliness. Vacuum energy gets appropriate low inside the crystal by its qubit binding energy, outside it is 10**20 higher. Scalar fields for color interaction/confinement and gravity could be derived from the qubit-interaction field.