Human/SARS-CoV-2 genome-scale metabolic modeling to discover potential antiviral targets for COVID-19

Discovery of a New Class of Thiazolidin-4-one-Based Inhibitors of Human Dihydroorotate Dehydrogenase: Biological Activity Evaluation, Molecular Docking, and Molecular Dynamics

The continuous outbreak of various viruses reminds us to prepare broad-spectrum antiviral drugs. Human dihydroorotate dehydrogenase (hDHODH) inhibitor exhibits broad-spectrum antiviral effects. In order to explore the novel type of human dihydroorotate dehydrogenase inhibitor (hDHODHi), we have optimized, designed, and synthesized 17 compounds and conducted biological activity evaluation, molecular docking, and molecular dynamics studies. The results of biological activity evaluation showed that compounds 10 and 16 exhibited submicromolar inhibitory activity, with IC50 values of 0.188 ± 0.004 and 0.593 ± 0.012 μM, respectively. Molecular docking studies showed that compounds 10 and 16 were in good agreement with the hDHODH activity pocket and interacted well with amino acid residues. Compared to the cocrystallized structure of the brequinar analogue complex, inhibitors 10 and 16 increased their direct interaction with Ala55. In addition, molecular dynamics studies showed that inhibitors 10 and 16 have strong affinity for proteins, and their complexes are stable, which confirms the significant inhibitory effect of inhibitors 10 and 16 on hDHODH in vitro. Through analysis, it was found that the carboxyl group and para introduced fluorine atoms in R 1, as well as the naphthalene in R 2, are key factors in improving activity. This conclusion provides help for further research into hDHODH inhibitors in the future. This study has promoted the significance of the development of broad-spectrum antiviral drugs.

Applications of genome-scale metabolic models to the study of human diseases: A systematic review

BACKGROUND AND OBJECTIVES

Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high-throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases.

METHODS

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Elsevier Scopus®, National Library of Medicine PubMed® and Clarivate Web of Science™ databases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined.

RESULTS

The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models.

CONCLUSIONS

The number of scientific papers, utilizing GEM-based approaches for the investigation of human diseases, suggests an increasing interest in these types of approaches; hopefully, the present review will represent a useful reference for scientists interested in applying computational modeling approaches to investigate the aetiopathology of human diseases; we also hope that this work will foster the development of novel applications and methods for the discovery of clinically-relevant insights on metabolic-related diseases.

Cell-specific genome-scale metabolic modeling of SARS-CoV-2-infected lung to identify antiviral enzymes

Computational systems biology plays a key role in the discovery of suitable antiviral targets. We designed a cell-specific, constraint-based modeling technique for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected lungs. We used the gene sequence of the alpha variant of SARS-CoV-2 to build a viral biomass reaction (VBR). We also used the mass proportion of lipids between the viral biomass and its host cell to estimate the stoichiometric coefficients of viral lipids in the reaction. We then integrated the VBR, the gene expression of the alpha variant of SARS-CoV-2, and the generic human metabolic network Recon3D to reconstruct a cell-specific genome-scale metabolic model. An antiviral target discovery (AVTD) platform was introduced using this model to identify therapeutic drug targets for combating COVID-19. The AVTD platform not only identified antiviral genes for eliminating viral replication but also predicted side effects of treatments. Our computational results revealed that knocking out dihydroorotate dehydrogenase (DHODH) might reduce the synthesis rate of cytidine-5'-triphosphate and uridine-5'-triphosphate, which terminate the viral building blocks of DNA and RNA for SARS-CoV-2 replication. Our results also indicated that DHODH is a promising antiviral target that causes minor side effects, which is consistent with the results of recent reports. Moreover, we discovered that the genes that participate in the de novo biosynthesis of glycerophospholipids and ceramides become unidentifiable if the VBR does not involve the stoichiometry of lipids.

Fuzzy optimization for identifying antiviral targets for treating SARS-CoV-2 infection in the heart

In this paper, a fuzzy hierarchical optimization framework is proposed for identifying potential antiviral targets for treating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the heart. The proposed framework comprises four objectives for evaluating the elimination of viral biomass growth and the minimization of side effects during treatment. In the application of the framework, Dulbecco's modified eagle medium (DMEM) and Ham's medium were used as uptake nutrients on an antiviral target discovery platform. The prediction results from the framework reveal that most of the antiviral enzymes in the aforementioned media are involved in fatty acid metabolism and amino acid metabolism. However, six enzymes involved in cholesterol biosynthesis in Ham's medium and three enzymes involved in glycolysis in DMEM are unable to eliminate the growth of the SARS-CoV-2 biomass. Three enzymes involved in glycolysis, namely BPGM, GAPDH, and ENO1, in DMEM combine with the supplemental uptake of L-cysteine to increase the cell viability grade and metabolic deviation grade. Moreover, six enzymes involved in cholesterol biosynthesis reduce and fail to reduce viral biomass growth in a culture medium if a cholesterol uptake reaction does not occur and occurs in this medium, respectively.

Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures

Genome-scale metabolic models (GEMs) have found numerous applications in different domains, ranging from biotechnology to systems medicine. Herein, we overview the most popular algorithms for the automated reconstruction of context-specific GEMs using high-throughput experimental data. Moreover, we describe different datasets applied in the process, and protocols that can be used to further automate the model reconstruction and validation. Finally, we describe recent COVID-19 applications of context-specific GEMs, focusing on the analysis of metabolic implications, identification of biomarkers and potential drug targets.

Glycerol-Based Retrievable Heterogeneous Catalysts for Single-Pot Esterification of Palm Fatty Acid Distillate to Biodiesel

The by-product of the previous transesterification, glycerol was utilised as an acid catalyst precursor for biodiesel production. The crude glycerol was treated through the sulfonation method with sulfuric acid and chlorosulfonic acid in a reflux batch reactor giving solid glycerol-SO3H and glycerol-ClSO3H, respectively. The synthesised acidic glycerol catalysts were characterised by various analytical techniques such as thermalgravimetric analyser (TGA), infrared spectroscopy, surface properties adsorption-desorption by nitrogen gas, ammonia-temperature programmed desorption (NH3-TPD), X-ray diffraction spectroscopy (XRD), elemental composition analysis by energy dispersive spectrometer (EDX) and surface micrographic morphologies by field emission electron microscope (FESEM). Both glycerol-SO3H and glycerol-ClSO3H samples exhibited mesoporous structures with a low surface area of 8.85 mm2/g and 4.71 mm2/g, respectively, supported by the microscopic image of blockage pores. However, the acidity strength for both catalysts was recorded at 3.43 mmol/g and 3.96 mmol/g, which is sufficient for catalysing PFAD biodiesel at the highest yield. The catalytic esterification was optimised at 96.7% and 98.2% with 3 wt.% of catalyst loading, 18:1 of methanol-PFAD molar ratio, 120 °C, and 4 h of reaction. Catalyst reusability was sustained up to 3 reaction cycles due to catalyst deactivation, and the insight investigation of spent catalysts was also performed.

Degradation of sulfamethoxazole in water by AgNbO3 photocatalyst mediated by persulfate

In this paper, silver niobate (AgNbO3) material was synthesized by a solid-state reaction. AgNbO3 was characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), UV-visible diffuse reflectance spectroscopy (DRS), and Brunauer-Emmett-Teller (BET) measurement. The photocatalytic activity of AgNbO3 was investigated in degradation of sulfamethoxazole (SMX) under visible light, which is a widely used antibiotic with significant threats towards health and aquatic organisms. Persulfate (PS) oxidant was found to improve the efficiency of the proposed photocatalytic removal of SMX by AgNbO3. The different operational parameters in the AgNbO3/PS/Vis system were investigated. The best photocatalytic performance was achieved with 0.5 g L-1 AgNbO3, 1.0 mM PS, and pH = 5.0 as the optimal conditions, achieving 98% of SMX degradation after 8 h of visible-light irradiation. Scavenger and electron spin resonance (ESR) experiments were carried out to identify the major reactive species in the SMX degradation and to propose the photocatalytic mechanism by the AgNbO3/PS/Vis system. The photodecomposition was found to be majorly caused by holes and ˙O2 - species, with ˙OH and SO4˙- radicals contributing to improve the photocatalytic process. The AgNbO3 catalyst was stable and reusable with efficient photocatalytic activity in three successive recycling experiments and its XRD patterns remained virtually unchanged. The reported process of PS activation by the AgNbO3 photocatalyst is promising for visible-light application in remediation of antibiotic-contaminated water.