Mucormycosis Metabolic Network Modeling: A Constraint-Based Approach

Mucormycosis is an uncommon illness caused by the fungus Mucorales. India was concerned about mucormycosis and COVID-19 in 2020. To minimize morbidity and occurrence, prevent, and treat mucormycosis, analysis is required. Combining systems biology and bioinformatics-based mucormycosis research, this study simulates the Genome-scale metabolic model (GSSM) of a Rhizopus oryzae strain for the comprehension of the organism's metabolic mechanism. Several key metabolic pathways for a mucormycosis-causing fungus strain were identified in research publications and targeted for inclusion in a model of a metabolic network. Based on the Flux Balance Analysis (FBA) approach, an integrated model of these pathways at the scale of the genome's metabolism was developed and appropriate constraints were applied to the numerous reactions involved in Rhizopus oryzae's metabolism using the COBRA package in MATLAB. Hence, unique evidence of pharmacological targets and biomarkers that may function as diagnostic, early analytic, and therapeutic agents in mucormycosis was discovered. Our study investigates the role of key metabolites in the model by applying constraints and altering fluxes, which provides valuable candidates for drug development. . © 2023 IEEE.

Systematizing Microbial Bioplastic Production for Developing Sustainable Bioeconomy: Metabolic Nexus Modeling, Economic and Environmental Technologies Assessment.

The excessive usage of non-renewable resources to produce plastic commodities has incongruously influenced the environment's health. Especially in the times of COVID-19, the need for plastic-based health products has increased predominantly. Given the rise in global warming and greenhouse gas emissions, the lifecycle of plastic has been established to contribute to it significantly. Bioplastics such as polyhydroxy alkanoates, polylactic acid, etc. derived from renewable energy origin have been a magnificent alternative to conventional plastics and reconnoitered exclusively for combating the environmental footprint of petrochemical plastic. However, the economically reasonable and environmentally friendly procedure of microbial bioplastic production has been a hard nut to crack due to less scouted and inefficient process optimization and downstream processing methodologies. Thereby, meticulous employment of computational tools such as genome-scale metabolic modeling and flux balance analysis has been practiced in recent times to understand the effect of genomic and environmental perturbations on the phenotype of the microorganism. In-silico results not only aid us in determining the biorefinery abilities of the model microorganism but also curb our reliance on equipment, raw materials, and capital investment for optimizing the best conditions. Additionally, to accomplish sustainable large-scale production of microbial bioplastic in a circular bioeconomy, extraction, and refinement of bioplastic needs to be investigated extensively by practicing techno-economic analysis and life cycle assessment. This review put forth state-of-the-art know-how on the proficiency of these computational techniques in laying the foundation of an efficient bioplastic manufacturing blueprint, chiefly focusing on microbial polyhydroxy alkanoates (PHA) production and its efficacy in outplacing fossil based plastic products.

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

BACKGROUND: Coronavirus disease 2019 (COVID-19) has caused a substantial increase in mortality and economic and social disruption. The absence of US Food and Drug Administration-approved drugs for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) highlights the need for new therapeutic drugs to combat COVID-19. METHODS: The present study proposed a fuzzy hierarchical optimization framework for identifying potential antiviral targets for COVID-19. The objectives in the decision-making problem were not only to evaluate the elimination of the virus growth, but also to minimize side effects causing treatment. The identified candidate targets could promote processes of drug discovery and development. SIGNIFICANT FINDINGS: Our gene-centric method revealed that dihydroorotate dehydrogenase (DHODH) inhibition could reduce viral biomass growth and metabolic deviation by 99.4% and 65.6%, respectively, and increase cell viability by 70.4%. We also identified two-target combinations that could completely block viral biomass growth and more effectively prevent metabolic deviation. We also discovered that the inhibition of two antiviral metabolites, cytidine triphosphate (CTP) and uridine-5'-triphosphate (UTP), exhibits effects similar to those of molnupiravir, which is undergoing phase III clinical trials. Our predictions also indicate that CTP and UTP inhibition blocks viral RNA replication through a similar mechanism to that of molnupiravir.

Genome-scale metabolic modelling of SARS-CoV-2 in cancer cells reveals an increased shift to glycolytic energy production.

Cancer is considered a high-risk condition for severe illness resulting from COVID-19. The interaction between severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and human metabolism is key to elucidating the risk posed by COVID-19 for cancer patients and identifying effective treatments, yet it is largely uncharacterised on a mechanistic level. We present a genome-scale map of short-term metabolic alterations triggered by SARS-CoV-2 infection of cancer cells. Through transcriptomic- and proteomic-informed genome-scale metabolic modelling, we characterise the role of RNA and fatty acid biosynthesis in conjunction with a rewiring in energy production pathways and enhanced cytokine secretion. These findings link together complementary aspects of viral invasion of cancer cells, while providing mechanistic insights that can inform the development of treatment strategies.

Optimization of a modeling platform to predict oncogenes from genome-scale metabolic networks of non-small-cell lung cancers.

Cancer cell dysregulations result in the abnormal regulation of cellular metabolic pathways. By simulating this metabolic reprogramming using constraint-based modeling approaches, oncogenes can be predicted, and this knowledge can be used in prognosis and treatment. We introduced a trilevel optimization problem describing metabolic reprogramming for inferring oncogenes. First, this study used RNA-Seq expression data of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) samples and their healthy counterparts to reconstruct tissue-specific genome-scale metabolic models and subsequently build the flux distribution pattern that provided a measure for the oncogene inference optimization problem for determining tumorigenesis. The platform detected 45 genes for LUAD and 84 genes for LUSC that lead to tumorigenesis. A high level of differentially expressed genes was not an essential factor for determining tumorigenesis. The platform indicated that pyruvate kinase (PKM), a well-known oncogene with a low level of differential gene expression in LUAD and LUSC, had the highest fitness among the predicted oncogenes based on computation. By contrast, pyruvate kinase L/R (PKLR), an isozyme of PKM, had a high level of differential gene expression in both cancers. Phosphatidylserine synthase 1 (PTDSS1), an oncogene in LUAD, was inferred to have a low level of differential gene expression, and overexpression could significantly reduce survival probability. According to the factor analysis, PTDSS1 characteristics were close to those of the template, but they were unobvious in LUSC. Angiotensin-converting enzyme 2 (ACE2) has recently garnered widespread interest as the SARS-CoV-2 virus receptor. Moreover, we determined that ACE2 is an oncogene of LUSC but not of LUAD. The platform developed in this study can identify oncogenes with low levels of differential expression and be used to identify potential therapeutic targets for cancer treatment.

Genome-Scale Metabolic Model of Infection with SARS-CoV-2 Mutants Confirms Guanylate Kinase as Robust Potential Antiviral Target.

The current SARS-CoV-2 pandemic is still threatening humankind. Despite first successes in vaccine development and approval, no antiviral treatment is available for COVID-19 patients. The success is further tarnished by the emergence and spreading of mutation variants of SARS-CoV-2, for which some vaccines have lower efficacy. This highlights the urgent need for antiviral therapies even more. This article describes how the genome-scale metabolic model (GEM) of the host-virus interaction of human alveolar macrophages and SARS-CoV-2 was refined by incorporating the latest information about the virus's structural proteins and the mutant variants B.1.1.7, B.1.351, B.1.28, B.1.427/B.1.429, and B.1.617. We confirmed the initially identified guanylate kinase as a potential antiviral target with this refined model and identified further potential targets from the purine and pyrimidine metabolism. The model was further extended by incorporating the virus' lipid requirements. This opened new perspectives for potential antiviral targets in the altered lipid metabolism. Especially the phosphatidylcholine biosynthesis seems to play a pivotal role in viral replication. The guanylate kinase is even a robust target in all investigated mutation variants currently spreading worldwide. These new insights can guide laboratory experiments for the validation of identified potential antiviral targets. Only the combination of vaccines and antiviral therapies will effectively defeat this ongoing pandemic.