DCcov: Repositioning of Drugs and Drug Combinations for SARS-CoV-2 Infected Lung through Constraint-Based Modelling

The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no effective antiviral drug except treatments for symptomatic therapy. Flux balance analysis is an efficient method to analyze metabolic networks. It allows optimizing for a metabolic function and thus e.g., predicting the growth rate of a specific cell or the production rate of a metabolite of interest. Here flux balance analysis was applied on human lung cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to reposition metabolic drugs and drug combinations against the replication of the SARS-CoV-2 virus within the host tissue. Making use of expression data sets of infected lung tissue, genome-scale COVID-19-specific metabolic models were reconstructed. Then host-specific essential genes and gene-pairs were determined through in-silico knockouts that permit reducing the viral biomass production without affecting the host biomass. Key pathways that are associated with COVID-19 severity in lung tissue are related to oxidative stress, as well as ferroptosis, sphingolipid metabolism, cysteine metabolism, and fat digestion. By in-silico screening of FDA approved drugs on the putative disease-specific essential genes and gene-pairs, 45 drugs and 99 drug combinations were predicted as promising candidates for COVID-19 focused drug repositioning (https://github.com/sysbiolux/DCcov). Among the 45 drug candidates, six antiviral drugs were found and seven drugs that are being tested in clinical trials against COVID-19. Other drugs like gemcitabine, rosuvastatin and acetylcysteine, and drug combinations like azathioprine-pemetrexed might offer new chances for treating COVID-19

A new compact wideband MIMO antenna – the double-sided tapered self-grounded monopole array

We present a new compact wideband multiple input multiple output (MIMO) antenna—the double-sided 4-port arm-tapered self-grounded monopole array, briefly referred to as the butterfly antenna, in the communication. The antenna is very compact with low correlation between ports and high diversity gain. The genetic algorithm optimization scheme has been employed in the design. Simulation results have been verified against measurements. The measured reflection coefficients at all ports are below -7 dB over 0.5–9 GHz and below -4.5 dB over 0.4–0.5 GHz and 9–15 GHz. The measured correlation coefficients are below 0.4 over 0.4–15 GHz and lower than 0.1 in most of the frequency band. This new MIMO antenna is developed as a transmit antenna in reverberation chambers, and we believe that it will find more applications in other systems, such as micro base station antennas in wireless communication systems

ANTIOXIDANT AND ANTIRADICAL ACTIVITY OF GREEN TEA (Camellia sinensis) AQUEOUS EXTRACT AND ITS CAPABILITY TO RETARDATION OF RATS LIVER CIRRHOSIS

he aim of the present study was to optimize the extraction conditions of green tea aqueous extract [green tea concentration (G) and extraction temperature (T)]. Response surface methodology was applied to determine the highest radical scavenging activity (RSA), Ferric reducing antioxidant power (FRAP) and reducing power (RP) of the prepared green tea extract. Effect of green tea aqueous extract prepared using the optimal conditions on the liver cirrhosis retardation in rats was also investigated. Two-factors central composite design was established to determine the effects of G or T and radical scavenging holding time as independent variables on RSA, FRAP and RP as dependent variables. The optimum G, T and holding time with maximum RSA were 1.0 %, 88.7 °C for 25 min, with a predicted RSA of 81.3 % (r2=0.9115) compared to the BHT, which had a scavenging value of 87.4 % at concentration 150 ppm and holding time 30 min The same predicted concentration and temperature obtained with the highest FRAP and RP were 2.566 and 1.687 with r2 0.9780 and 0.9550, respectively. The phenolic and flavonoid contents were 81.2 mg gallic acid equivalent and 33.5 mg quercetin equivalent per 100 ml green tea extract. The extract prepared at optimal conditions was used for treatment of cirrhotic rats by CCl4. Insignificant (P≥0.05) differences were observed between the green tea group and control group in obtained total protein or albumin values. Total protein and albumin were dramatically decreased in the group treated by CCL4.  The same trend was observed with studying the transaminase enzymes. Histopathological sections appeared the effect of green tea extract on the retardation of liver cirrhosis in rats

Bruceine D Identified as a Drug Candidate against Breast Cancer by a Novel Drug Selection Pipeline and Cell Viability Assay.

The multi-target effects of natural products allow us to fight complex diseases like cancer on multiple fronts. Unlike docking techniques, network-based approaches such as genome-scale metabolic modelling can capture multi-target effects. However, the incompleteness of natural product target information reduces the prediction accuracy of in silico gene knockout strategies. Here, we present a drug selection workflow based on context-specific genome-scale metabolic models, built from the expression data of cancer cells treated with natural products, to predict cell viability. The workflow comprises four steps: first, in silico single-drug and drug combination predictions; second, the assessment of the effects of natural products on cancer metabolism via the computation of a dissimilarity score between the treated and control models; third, the identification of natural products with similar effects to the approved drugs; and fourth, the identification of drugs with the predicted effects in pathways of interest, such as the androgen and estrogen pathway. Out of the initial 101 natural products, nine candidates were tested in a 2D cell viability assay. Bruceine D, emodin, and scutellarein showed a dose-dependent inhibition of MCF-7 and Hs 578T cell proliferation with IC(50) values between 0.7 to 65 μM, depending on the drug and cell line. Bruceine D, extracted from Brucea javanica seeds, showed the highest potency

Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases.

Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for these and other diseases. Many recent studies focused on enhancing, among others, drug predictions by generating generic metabolic models of brain cells and on the contextualisation of the genome-scale metabolic models with expression data. Experimental flux rates were primarily used to constrain or validate the model inputs. Bi-cellular models were reconstructed to study the interaction between different cell types. This review highlights the evolution of genome-scale models for neurodegenerative diseases and glioma. We discuss the advantages and drawbacks of each approach and propose improvements, such as building bi-cellular models, tailoring the biomass formulations for glioma and refinement of the cerebrospinal fluid composition

The H3ABioNet helpdesk: an online bioinformatics resource, enhancing Africa’s capacity for genomics research

Abstract Background Currently, formal mechanisms for bioinformatics support are limited. The H3Africa Bioinformatics Network has implemented a public and freely available Helpdesk (HD), which provides generic bioinformatics support to researchers through an online ticketing platform. The following article reports on the H3ABioNet HD (H3A-HD)‘s development, outlining its design, management, usage and evaluation framework, as well as the lessons learned through implementation. Results The H3A-HD evaluated using automatically generated usage logs, user feedback and qualitative ticket evaluation. Evaluation revealed that communication methods, ticketing strategies and the technical platforms used are some of the primary factors which may influence the effectivity of HD. Conclusion To continuously improve the H3A-HD services, the resource should be regularly monitored and evaluated. The H3A-HD design, implementation and evaluation framework could be easily adapted for use by interested stakeholders within the Bioinformatics community and beyond

Project-based learning course on metabolic network modelling in computational systems biology.

Project-based learning (PBL) is a dynamic student-centred teaching method that encourages students to solve real-life problems while fostering engagement and critical thinking. Here, we report on a PBL course on metabolic network modelling that has been running for several years within the Master in Integrated Systems Biology (MISB) at the University of Luxembourg. This 2-week full-time block course comprises an introduction into the core concepts and methods of constraint-based modelling (CBM), applied to toy models and large-scale networks alongside the preparation of individual student projects in week 1 and, in week 2, the presentation and execution of these projects. We describe in detail the schedule and content of the course, exemplary student projects, and reflect on outcomes and lessons learned. PBL requires the full engagement of students and teachers and gives a rewarding teaching experience. The presented course can serve as a role model and inspiration for other similar courses

Towards Effective Prediction of Repurposable Drugs Through Metabolic Modeling: Evaluation Against Approved Drugs Using Preclinical and Clinical Data

Preclinical models in gliomas —the most prevalent adult brain cancer that originate from glial cells —are either inaccurate in capturing metabolic dependencies in humans or hard to culture. The 2021 World Health Organization classification of the central nervous system tumors stratifies adult gliomas into three subtypes: IDH-mutant astrocytoma, oligodendroglioma and IDH-wildtype glioblastoma (GBM). The limited preclinical models in IDH-mutant gliomas and the poor five-year survival of 7% in GBM limit investigational drugs and the success rate of clinical trials, respectively. These clinical and preclinical obstacles resulted in few approved monotherapies that primarily target the cell cycle and approved combinations with redundant pathways. Therefore, the need for efficacious drugs and combinations targeting alternative pathways is pivotal. Drug repurposing, i.e., redirecting approved drugs to other diseases, has been critical in shortening the lengthy toxicity trials in cancer drug discovery. Among the computational drug repurposing approaches, metabolic modeling enables the simulation of the cellular metabolism using the well-annotated biochemical network with interpretable and accurate target identification. Our review of genome-scale metabolic models (GEMs) in the brain showed glioma GEMs only cover GBM, which are either built on a non-genome scale or lack curation. Here, we present GEMs of the three well-defined glioma subtypes built with the rFASTCORMICS algorithm that predicted repurposable FDA-approved single drugs and combinations for gliomas. Predicted drugs showed comparable efficacy to approved drugs using published in vitro and xenograft drug screenings. The oligodendroglioma metabolic model replicated the metabolic exchanges of the hard-to-culture oligodendroglioma preclinical models. Similarly, two novel predicted combinations of non-cancer drugs were coherent with the known dependencies in IDH-wildtype and -mutant gliomas. Finally, four predicted drugs showed comparable survival as monotherapy or improved survival combined with approved drugs compared to the approved drug arm in phase I/II glioma clinical trials. The previous drug prediction pipeline was also applied to repurpose approved drugs to COVID-19 and melanoma and natural products to breast cancer. Predicted drugs for melanoma, breast cancer and COVID-19 targeting cholesterol synthesis, estrogen metabolism, and cysteine synthesis, respectively, reduced mortality/incidence in their respective diseases. Unlike melanoma-specific cholesterol synthesis, the glioma GEMs accurately captured the in vivo dependency of cholesterol esterification and avoided the in vitro cholesterol synthesis dependency that failed in a clinical trial. Overall, this work provides an overview of how metabolic modeling can be used to detect biomarkers and repurpose drugs, where metabolic modeling was competitive with preclinical methods and could predict new drugs.3. Good health and well-bein

DCcov: Repositioning of drugs and drug combinations for SARS-CoV-2 infected lung through constraint-based modeling.

The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no approved effective antiviral drug. Flux balance analysis (FBA) is an efficient method to analyze metabolic networks. Here, FBA was applied on human lung cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to reposition metabolic drugs and drug combinations against the virus replication within the host tissue. Making use of expression datasets of infected lung tissue, genome-scale COVID-19-specific metabolic models were reconstructed. Then, host-specific essential genes and gene pairs were determined through in silico knockouts that permit reducing the viral biomass production without affecting the host biomass. Key pathways that are associated with COVID-19 severity in lung tissue are related to oxidative stress, ferroptosis, and pyrimidine metabolism. By in silico screening of Food and Drug Administration (FDA)-approved drugs on the putative disease-specific essential genes and gene pairs, 85 drugs and 52 drug combinations were predicted as promising candidates for COVID-19 (https://github.com/sysbiolux/DCcov)