Integration of omics data to generate and analyse COVID-19 specific genome-scale metabolic models

Režen, Tadeja; Martins, Anelise Afonso; Mraz, Miha; Zimic, Nikolaj; Rozman, Damjana; Moškon, Miha

doi:10.1016/j.compbiomed.2022.105428

Cited by 7 publications

(7 citation statements)

References 92 publications

(128 reference statements)

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“…The latter have employed different types of experimental data, such as data obtained from different cell lines (e.g., see [ 74 ]), patient samples (e.g., see [ 108 ]), or a combinations of both (e.g., [ 102 ]). Most of the approaches devoted to the GEM-based analysis of COVID-19 focused on the identification of enriched metabolic reactions and pathways (e.g., see [ 102 , 106 , 107 ]) and/or to the identification of potential antiviral targets (e.g., see [ 98 , 99 , 105 ]) and drug repurposing (e.g., see [ 100 , 103 ]). Reported studies also focused on the identification of metabolic reactions guiding disease severity [ 108 ], and on the analysis of cancer metabolism affected by SARS-CoV-2 infection [ 74 ].…”

Section: Discussionmentioning

confidence: 99%

“…Režen et al [ 107 ] performed an extension of the context-specific GEM analysis described by Nanda and Ghosh [ 106 ]. They extended the analysed datasets to also include human embryonic kidney (293T), Calu-3, and adenocarcinoma human alveolar basal epithelial (A549) cell lines [ 118 ].…”

Section: Covid-19 Applications Of Context-specific Genome-scale Metab...mentioning

confidence: 99%

“…Cheng et al [ 102 ] performed the integration of 12 published gene expression datasets on SARS-CoV-2 infection (3 of which were also analysed in [ 107 ]) using the IMAT algorithm, and Recon 1 [ 19 ] and Recon3D GEM [ 21 ]. Metabolic flux distributions were obtained with flux sampling and differential fluxes were assessed between the healthy and infected models.…”

Section: Covid-19 Applications Of Context-specific Genome-scale Metab...mentioning

confidence: 99%

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

Moškon

Režen

2023

Metabolites

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Covid-19 Applications Of Context-specific Genome-scale Metab...mentioning

confidence: 99%

Section: Covid-19 Applications Of Context-specific Genome-scale Metab...mentioning

confidence: 99%

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

Moškon

Režen

2023

Metabolites

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“…ANPELA 1.0 has become popular and indispensable as an instructive tool in quantitative bulk proteomics for disease prediction, [1][2][3] biomarker discovery, [4][5][6] innovative drug target identification, [7,8] novel peptide exploration, [9,10] experimental scheme establishment, [11] bioinformatic algorithm comparison, and development. [12][13][14] Although bulk proteome profiling has become progressively quantitative and comprehensive, [12,15] it has historically been limited to the relatively large sample cohorts required to satisfy an in-depth measurement, which typically represents a population average and obscure significant cellular heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

ANPELA: Significantly Enhanced Quantification Tool for Cytometry‐Based Single‐Cell Proteomics

et al. 2023

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ANPELA is widely used for quantifying traditional bulk proteomic data. Recently, there is a clear shift from bulk proteomics to the single-cell ones (SCP), for which powerful cytometry techniques demonstrate the fantastic capacity of capturing cellular heterogeneity that is completely overlooked by traditional bulk profiling. However, the in-depth and high-quality quantification of SCP data is still challenging and severely affected by the large numbers of quantification workflows and extreme performance dependence on the studied datasets. In other words, the proper selection of well-performing workflow(s) for any studied dataset is elusory, and it is urgently needed to have a significantly enhanced and accelerated tool to address this issue. However, no such tool is developed yet. Herein, ANPELA is therefore updated to its 2.0 version (https://idrblab.org/anpela/), which is unique in providing the most comprehensive set of quantification alternatives (>1000 workflows) among all existing tools, enabling systematic performance evaluation from multiple perspectives based on machine learning, and identifying the optimal workflow(s) using overall performance ranking together with the parallel computation. Extensive validation on different benchmark datasets and representative application scenarios suggest the great application potential of ANPELA in current SCP research for gaining more accurate and reliable biological insights.

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“…These models were then used to perform metabolic analysis and gene knockdown experiments. Other studies have constructed separate metabolic models of normal host cells and SARS-CoV-2-infected host cells [25,26], and analyzed the changes in host cell metabolic systems before and after infection using flux variability analysis [27] (FVA) or flux sampling [28]. This approach focuses on metabolic pathways and subsystems with significant differences in fluxes between normal and infected cells.…”

Section: Introductionmentioning

confidence: 99%

A target prediction method to inhibit the replication process of SARS-CoV-2 via metabolic differential analysis

Zheng

Zhao

2023

Preprint

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The emergence and rapid spread of COVID-19 has had a tremendous impact on people's lives, and it is therefore necessary to study how to treat COVID-19. SARS-CoV-2 invades and reprograms human cells, altering the host cell metabolic system to favor its survival and replication, ultimately leading to disease onset. However, few studies have focused on inhibiting the replication process of SARS-CoV-2. In this paper, we present a novel method to inhibit SARS-CoV-2 replication. Our method reconstructs network models of the host cell metabolic systems altered by virus invasion and predicts candidate antiviral targets via metabolic differential analysis. We separately analyzed gene expression data from lung and non-lung host cells to perform target prediction. The results indicate that D-alanine is a key metabolite affecting SARS-CoV-2 replication. Our approach is general and applicable to existing viruses, offering new ideas for dealing with viral diseases.

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Integration of omics data to generate and analyse COVID-19 specific genome-scale metabolic models

Cited by 7 publications

References 92 publications

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

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

ANPELA: Significantly Enhanced Quantification Tool for Cytometry‐Based Single‐Cell Proteomics

A target prediction method to inhibit the replication process of SARS-CoV-2 via metabolic differential analysis

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