Transcriptomics-driven metabolic pathway analysis reveals similar alterations in lipid metabolism in mouse MASH model and human DOI Creative Commons
Sofia Tsouka, Pavitra Kumar, Patcharamon Seubnooch

et al.

Communications Medicine, Journal Year: 2024, Volume and Issue: 4(1)

Published: March 5, 2024

Abstract Background Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent chronic worldwide, and can rapidly progress to metabolic steatohepatitis (MASH). Accurate preclinical models methodologies are needed understand underlying mechanisms develop treatment strategies. Through meta-analysis of currently proposed mouse models, we hypothesized that diet- chemical-induced MASH model closely resembles the observed lipid metabolism alterations in humans. Methods We developed transcriptomics-driven pathway analysis (TDMPA), method aid evaluation resemblance. TDMPA uses genome-scale calculate enzymatic reaction perturbations from gene expression data. performed score compare human signatures. used an already-established WD+CCl4-induced functional assays lipidomics confirm findings. Results Both exhibit numerous altered pathways, including triglyceride biosynthesis, fatty acid beta-oxidation, bile cholesterol metabolism, oxidative phosphorylation. significant reduction mitochondrial functions bioenergetics, as well acylcarnitines for model. identify wide range species within most perturbed pathways predicted by TDMPA. Triglycerides, phospholipids, acids increased significantly liver, confirming our initial observations. Conclusions introduce TDMPA, methodology evaluating disorders. By comparing signatures typify MASH, show good resemblance WD+CCl4 Our presented approach provides valuable tool defining space experimental design assessing metabolism.

Language: Английский

Role of Glutathione in Cancer: From Mechanisms to Therapies DOI Creative Commons
Luke Kennedy, Jagdeep K. Sandhu, Mary‐Ellen Harper

et al.

Biomolecules, Journal Year: 2020, Volume and Issue: 10(10), P. 1429 - 1429

Published: Oct. 9, 2020

Glutathione (GSH) is the most abundant non-protein thiol present at millimolar concentrations in mammalian tissues. As an important intracellular antioxidant, it acts as a regulator of cellular redox state protecting cells from damage caused by lipid peroxides, reactive oxygen and nitrogen species, xenobiotics. Recent studies have highlighted importance GSH key signal transduction reactions controller cell differentiation, proliferation, apoptosis, ferroptosis immune function. Molecular changes antioxidant system disturbances homeostasis been implicated tumor initiation, progression, treatment response. Hence, has both protective pathogenic roles. Although healthy crucial for removal detoxification carcinogens, elevated levels are associated with progression increased resistance to chemotherapeutic drugs. Recently, several novel therapies developed target tumors means response decreased drug resistance. In this comprehensive review we explore mechanisms functionalities different therapeutic approaches that either directly, indirectly or use GSH-based prodrugs. Consideration also given computational methods used describe related processes silico testing effects.

Language: Английский

Citations

603
SpaceM reveals metabolic states of single cells DOI
Luca Rappez, Mira Stadler, Sergio Triana

et al.

Nature Methods, Journal Year: 2021, Volume and Issue: 18(7), P. 799 - 805

Published: July 1, 2021

Language: Английский

Citations

257
Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction DOI Creative Commons
Feiran Li, Le Yuan, Hongzhong Lu

et al.

Nature Catalysis, Journal Year: 2022, Volume and Issue: 5(8), P. 662 - 672

Published: June 16, 2022

Abstract Enzyme turnover numbers ( k cat ) are key to understanding cellular metabolism, proteome allocation and physiological diversity, but experimentally measured data sparse noisy. Here we provide a deep learning approach (DLKcat) for high-throughput prediction metabolic enzymes from any organism merely substrate structures protein sequences. DLKcat can capture changes mutated identify amino acid residues with strong impact on values. We applied this predict genome-scale values more than 300 yeast species. Additionally, designed Bayesian pipeline parameterize enzyme-constrained models predicted The resulting outperformed the corresponding original previous pipelines in predicting phenotypes proteomes, enabled us explain phenotypic differences. model construction valuable tools uncover global trends of enzyme kinetics further elucidate metabolism large scale.

Language: Английский

Citations

251
A high-stringency blueprint of the human proteome DOI Creative Commons
Subash Adhikari, Edouard C. Nice, Eric W. Deutsch

et al.

Nature Communications, Journal Year: 2020, Volume and Issue: 11(1)

Published: Oct. 16, 2020

Abstract The Human Proteome Organization (HUPO) launched the Project (HPP) in 2010, creating an international framework for global collaboration, data sharing, quality assurance and enhancing accurate annotation of genome-encoded proteome. During subsequent decade, HPP established collaborations, developed guidelines metrics, undertook reanalysis previously deposited community data, continuously increasing coverage human On occasion HPP’s tenth anniversary, we here report a 90.4% complete high-stringency proteome blueprint. This knowledge is essential discerning molecular processes health disease, as demonstrate by highlighting potential roles plays our understanding, diagnosis treatment cancers, cardiovascular infectious diseases.

Language: Английский

Citations

199
Prior and novel coronaviruses, Coronavirus Disease 2019 (COVID-19), and human reproduction: what is known? DOI Creative Commons
James H. Segars, Quinton S. Katler, Dana B. McQueen

et al.

Fertility and Sterility, Journal Year: 2020, Volume and Issue: 113(6), P. 1140 - 1149

Published: April 16, 2020

Language: Английский

Citations

183
The Human Protein Atlas—Spatial localization of the human proteome in health and disease DOI Open Access
Andreas Digre, Cecilia Lindskog

Protein Science, Journal Year: 2020, Volume and Issue: 30(1), P. 218 - 233

Published: Nov. 4, 2020

Abstract For a complete understanding of system's processes and each protein's role in health disease, it is essential to study protein expression with spatial resolution, as the exact location proteins at tissue, cellular, or subcellular levels tightly linked function. The Human Protein Atlas (HPA) project large‐scale initiative aiming mapping entire human proteome using antibody‐based proteomics integration various other omics technologies. publicly available knowledge resource www.proteinatlas.org one world's most visited biological databases has been extensively updated during last few years. current version divided into six main sections, focusing on particular aspects proteome: (a) Tissue showing distribution across all major tissues organs body; (b) Cell localization single cells; (c) Pathology impact survival patients cancer; (d) Blood profiles blood cells actively secreted proteins; (e) Brain human, mouse, pig brain; (f) Metabolic involvement metabolism. HPA constitutes an important for further biology, datasets hold much promise emerging efforts cell analyses, both transcriptomic proteomic level.

Language: Английский

Citations

156
Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0 DOI Creative Commons
Iván Domenzain, Benjamín J. Sánchez,

Mihail Anton

et al.

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: June 30, 2022

Abstract Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration enzyme constraints proteomics data into such was first enabled by GECKO toolbox, allowing study phenotypes constrained protein limitations. Here, we upgrade toolbox in order to enhance with any organism a compatible GEM reconstruction. With this, enzyme-constrained budding yeasts Saccharomyces cerevisiae , Yarrowia lipolytica Kluyveromyces marxianus are generated their long-term adaptation several stress factors incorporation data. Predictions reveal that upregulation high saturation enzymes amino acid metabolism common across organisms conditions, suggesting relevance robustness contrast optimal utilization as cellular objective microbial growth under nutrient-limited conditions. The functionality is expanded an automated framework continuous version-controlled update GEMs, also producing Escherichia coli Homo sapiens . In this work, facilitate GEMs basic science, engineering synthetic biology purposes.

Language: Английский

Citations

99
Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0 DOI
Yu Chen, Johan Gustafsson, Albert Tafur Rangel

et al.

Nature Protocols, Journal Year: 2024, Volume and Issue: 19(3), P. 629 - 667

Published: Jan. 18, 2024

Language: Английский

Citations

33
Abnormal brain cholesterol homeostasis in Alzheimer’s disease—a targeted metabolomic and transcriptomic study DOI Creative Commons
Vijay R. Varma, Hatice Büşra Lüleci̇, Anup Mammen Oommen

et al.

npj Aging and Mechanisms of Disease, Journal Year: 2021, Volume and Issue: 7(1)

Published: June 1, 2021

The role of brain cholesterol metabolism in Alzheimer's disease (AD) remains unclear. Peripheral and levels are largely independent due to the impermeability blood barrier (BBB), highlighting importance studying homeostasis AD. We first tested whether metabolite markers biosynthesis catabolism were altered AD associated with pathology using linear mixed-effects models two autopsy samples from Baltimore Longitudinal Study Aging (BLSA) Religious Orders (ROS). next genetic regulators ANOVA test publicly available tissue transcriptomic datasets. Finally, regional data, we performed genome-scale metabolic network modeling assess alterations reactions show that is pervasive abnormalities catabolism. Using data Parkinson's (PD) samples, found gene expression identified not observed PD, suggesting these changes may be specific Our results suggest reduced de novo occur response impaired enzymatic efflux maintain This accompanied by accumulation nonenzymatically generated cytotoxic oxysterols. set stage for experimental studies address plausible therapeutic targets

Language: Английский

Citations

104
Deep learning meets metabolomics: a methodological perspective DOI
Partho Sen, Santosh Lamichhane, Vivek Bhakta Mathema

et al.

Briefings in Bioinformatics, Journal Year: 2020, Volume and Issue: 22(2), P. 1531 - 1542

Published: Aug. 11, 2020

Deep learning (DL), an emerging area of investigation in the fields machine and artificial intelligence, has markedly advanced over past years. DL techniques are being applied to assist medical professionals researchers improving clinical diagnosis, disease prediction drug discovery. It is expected that will help provide actionable knowledge from a variety 'big data', including metabolomics data. In this review, we discuss applicability metabolomics, while presenting discussing several examples recent research. We emphasize use tackling bottlenecks data acquisition, processing, metabolite identification, as well metabolic phenotyping biomarker Finally, how used genome-scale modelling interpretation The DL-based approaches discussed here may computational biologists with integration, drawing statistical inference about biological outcomes, based on

Language: Английский

Citations

94