Role of a-Synuclein in Cell Biology: A Hypothesis and its Implications in Neurodegenerative Diseases

I wish to suggest a physiological function for alpha-synuclein (a-syn) that has the potential to explain its role in pathology. Intraneuronal proteinaceous Lewy Bodies (LBs), the pathological hallmark of Parkinson’s disease and other synucleinopathies, consist majorly of a-syn. Ample evidence suggests that LBs are not the result of simple amyloidosis of cytosolic a-syn. Benign soluble unstructured a-syn gets converted into toxic species which preferentially accumulates in LBs. But how these aberrant a-syn molecules are produced in the cytosol, is still not clear. The present hypothesis is an effort to relate a metabolic reaction specific to neuronal function, that is, phase transition, with the pathobiology of a-syn. During high frequency stimulation, which entails rapid phase transition reactions at the presynaptic compartment, aberrant interaction of a-syn with the membrane occasionally generates toxic a-syn molecules. My conjecture is that the physiological function of a-syn is to modulate membrane fluidity by a process wherein it goes through a conformation cycle driven by a flux of energy from mitochondria. It is the range of toxic a-syn produced during aberrant phase transition reaction that is responsible for pathology, not the normal a-syn that reenters the conformation cycle, thereby, resolving the paradox of the Janus-face of a-syn.

Remdesivir: Mechanism of Metabolic Conversion from Prodrug to Drug

Background: Remdesivir (GS-5734) has emerged as a promising drug during the challenging times of COVID-19 pandemic. Being a prodrug, it undergoes several metabolic reactions before converting to its active triphosphate metabolite. It is important to establish the atomic level details and explore the energy profile of the prodrug to drug conversion process. Methods: In this work, Density Functional Theory (DFT) calculations were performed to explore the entire metabolic path. Further, the potential energy surface (PES) diagram for the conversion of prodrug remdesivir to its active metabolite was established. The role of catalytic triad of Hint1 phosphoramidase enzyme in P-N bond hydrolysis was also studied on a model system using combined molecular docking and quantum mechanics approach. Results: The overall energy of reaction is 11.47 kcal/mol exergonic and the reaction proceeds through many steps requiring high activation energies. In the absence of a catalyst, the P-N bond breaking step requires 41.78 kcal/mol, which is reduced to 14.26 kcal/mol in a catalytic environment. Conclusion: The metabolic pathways of model system of remdesivir (MSR) were completely explored completely and potential energy surface diagrams at two levels of theory, B3LYP/6-311++G(d, p) and B3LYP/6-31+G(d), were established and compared. The results highlight the importance of an additional water molecule in the metabolic reaction. The P-N bond cleavage step of the metabolic process requires the presence of an enzymatic environment.

Understanding Metabolic Flux Behaviour in Whole-Cell Model Output

Whole-cell modelling is a newly expanding field that has many applications in lab experiment design and predictive drug testing. Although whole-cell model output contains a wealth of information, it is complex and high dimensional and thus hard to interpret. Here, we present an analysis pipeline that combines machine learning, dimensionality reduction, and network analysis to interpret and visualise metabolic reaction fluxes from a set of single gene knockouts simulated in the Mycoplasma genitalium whole-cell model. We found that the reaction behaviours show trends that correlate with phenotypic classes of the simulation output, highlighting particular cellular subsystems that malfunction after gene knockouts. From a graphical representation of the metabolic network, we saw that there is a set of reactions that can be used as markers of a phenotypic class, showing their importance within the network. Our analysis pipeline can support the understanding of the complexity of in silico cells without detailed knowledge of the constituent parts, which can help to understand the effects of gene knockouts and, as whole-cell models become more widely built and used, aid genome design.

Antioxidative Role of Synthetic and Natural Ascorbic Acid (Vitamin C) in Metabolic Pathways of A Biological System

The properties of a substance are determined by the structure of its component molecules. Ascorbic acid occurs abundantly in fresh fruit, especially blackcurrants, citrus fruit and strawberries, and in most fresh vegetables; good sources are broccoli and peppers. It is destroyed by heat and is not well stored in the body3. Ascorbic acid is a good reducing agent and facilitates many metabolic reaction and repair processes. In pharmaceutical preparations and fruit juices, ascorbic acid is readily separated from other compounds by TLC on silica gel and quantitated directly by absorption at 254nm. Serum and plasma may be deproteinized with twice the volume of methanol or ethanol.

Anti-Inflammatory Potential of Daturaolone from Datura innoxia Mill.: In Silico, In Vitro and In Vivo Studies

Exploration of leads with therapeutic potential in inflammatory disorders is worth pursuing. In line with this, the isolated natural compound daturaolone from Datura innoxia Mill. was evaluated for its anti-inflammatory potential using in silico, in vitro and in vivo models. Daturaolone follows Lipinski’s drug-likeliness rule with a score of 0.33. Absorption, distribution, metabolism, excretion and toxicity prediction show strong plasma protein binding; gastrointestinal absorption (Caco-2 cells permeability = 34.6 nm/s); no blood–brain barrier penetration; CYP1A2, CYP2C19 and CYP3A4 metabolism; a major metabolic reaction, being aliphatic hydroxylation; no hERG inhibition; and non-carcinogenicity. Predicted molecular targets were mainly inflammatory mediators. Molecular docking depicted H-bonding interaction with nuclear factor kappa beta subunit (NF-κB), cyclooxygenase-2, 5-lipoxygenase, phospholipase A2, serotonin transporter, dopamine receptor D1 and 5-hydroxy tryptamine. Its cytotoxicity (IC50) value in normal lymphocytes was >20 µg/mL as compared to cancer cells (Huh7.5; 17.32 ± 1.43 µg/mL). Daturaolone significantly inhibited NF-κB and nitric oxide production with IC50 values of 1.2 ± 0.8 and 4.51 ± 0.92 µg/mL, respectively. It significantly reduced inflammatory paw edema (81.73 ± 3.16%), heat-induced pain (89.47 ± 9.01% antinociception) and stress-induced depression (68 ± 9.22 s immobility time in tail suspension test). This work suggests a possible anti-inflammatory role of daturaolone; however, detailed mechanistic studies are still necessary to corroborate and extrapolate the findings.

Signal-Processing and Adaptive Prototissue Formation in Metabolic DNA Protocells

The fundamental life-defining processes in living cells, such as replication, division, adaptation, and tissue formation, take place via intertwined metabolic reaction networks orchestrating downstream signal processing in a confined, crowded environment with high precision. Hence, it is crucial to understand and reenact some of these functions in wholly synthetic cell-like entities (protocells) to envision designing soft-materials with life-like traits. Herein, we report on a programmable all-DNA protocell (PC) composed of a liquid DNA interior and a hydrogel-like shell, harboring DNAzyme active sites in the interior whose catalytic bond-cleaving activity leads to a downstream phenotype change in the protocells, as well as triggers prototissue formation. In this regard, we coupled several tools of DNA nanoscience, such as RNA cleavage, dynamic strand displacement reactions, and multivalent palindromic interactions, in a synchronize pathway so that the input signal can be processed inside the protocells and generate downstream cues giving rise to metabolic adaptive behavior. For example, the compartmentalized DNAzyme catalyzes the bond-cleavage of a substrate that releases a DNA strand in situ to trigger a strand displacement reaction at the shell of the protocells leading to a change in color resembling a “phenotype-like” change in cells, and finally to establish communication with other protocells via multivalent interactions.

Signal-Processing and Adaptive Prototissue Formation in Metabolic DNA Protocells

The fundamental life-defining processes in living cells, such as replication, division, adaptation, and tissue formation, take place via intertwined metabolic reaction networks orchestrating downstream signal processing in a confined, crowded environment with high precision. Hence, it is crucial to understand and reenact some of these functions in wholly synthetic cell-like entities (protocells) to envision designing soft-materials with life-like traits. Herein, we report on a programmable all-DNA protocell (PC) composed of a liquid DNA interior and a hydrogel-like shell, harboring DNAzyme active sites in the interior whose catalytic bond-cleaving activity leads to a downstream phenotype change in the protocells, as well as triggers prototissue formation. In this regard, we coupled several tools of DNA nanoscience, such as RNA cleavage, dynamic strand displacement reactions, and multivalent palindromic interactions, in a synchronize pathway so that the input signal can be processed inside the protocells and generate downstream cues giving rise to metabolic adaptive behavior. For example, the compartmentalized DNAzyme catalyzes the bond-cleavage of a substrate that releases a DNA strand in situ to trigger a strand displacement reaction at the shell of the protocells leading to a change in color resembling a “phenotype-like” change in cells, and finally to establish communication with other protocells via multivalent interactions.

Metabolic Reaction Network-Based Model Predictive Control of Bioprocesses

Bioprocesses are increasingly used for the production of high added value products. Microorganisms are used in bioprocesses to mediate or catalyze the necessary reactions. This makes bioprocesses highly nonlinear and the governing mechanisms are complex. These complex governing mechanisms can be modeled by a metabolic network that comprises all interactions within the cells of the microbial population present in the bioprocess. The current state of the art in bioprocess control is model predictive control based on the use of macroscopic models, solely accounting for substrate, biomass, and product mass balances. These macroscopic models do not account for the underlying mechanisms governing the observed process behavior. Consequently, opportunities are missed to fully exploit the available process knowledge to operate the process in a more sustainable manner. In this article, a procedure is presented for metabolic network-based model predictive control. This procedure uses a combined moving horizon-model predictive control strategy to monitor the flux state and optimize the bioprocess under study. A CSTR bioreactor model has been combined with a small-scale metabolic network to illustrate the performance of the presented procedure.

Controlled metabolic cascades for protein synthesis in an artificial cell

In recent years, researchers have been pursuing a method to design and to construct life forms from scratch — in other words, to create artificial cells. In many studies, artificial cellular membranes have been successfully fabricated, allowing the research field to grow by leaps and bounds. Moreover, in addition to lipid bilayer membranes, proteins are essential factors required to construct any cellular metabolic reaction; for that reason, different cell-free expression systems under various conditions to achieve the goal of controlling the synthetic cascades of proteins in a confined area have been reported. Thus, in this review, we will discuss recent issues and strategies, enabling to control protein synthesis cascades that are being used, particularly in research on artificial cells.