Model-driven insights into the effects of temperature on metabolism DOI Creative Commons
Philipp Wendering, Zoran Nikoloski

Biotechnology Advances, Journal Year: 2023, Volume and Issue: 67, P. 108203 - 108203

Published: June 20, 2023

Temperature affects cellular processes at different spatiotemporal scales, and identifying the genetic molecular mechanisms underlying temperature responses paves way to develop approaches for mitigating effects of future climate scenarios. A systems view on physiology can be obtained by focusing metabolism since: (i) its functions depend transcription translation (ii) outcomes support organisms' development, growth, reproduction. Here we provide a systematic review modelling efforts directed investigating properties single biochemical reactions, system-level traits, metabolic subsystems, whole-cell across prokaryotes eukaryotes. We compare contrast computational theories that facilitate key enzymes their consideration in constraint-based as well kinetic models metabolism. In addition, summary insights from approaches, facilitating integration omics data temperature-modulated experiments with networks, resulting biotechnological applications. Lastly, perspective how types profit developments machine learning layers improve model-driven into relevant

Language: Английский

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

261
Mitochondrial ATP generation is more proteome efficient than glycolysis DOI
Yihui Shen, Hoang V. Dinh, Edward R. Cruz

et al.

Nature Chemical Biology, Journal Year: 2024, Volume and Issue: 20(9), P. 1123 - 1132

Published: March 6, 2024

Language: Английский

Citations

37
A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics DOI Creative Commons
Omid Oftadeh, Pierre Salvy, María Masid

et al.

Nature Communications, Journal Year: 2021, Volume and Issue: 12(1)

Published: Aug. 9, 2021

Eukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these systems, a mathematical approach can be used integrate description multiple biological networks into single model for cell analysis engineering. One most accurate models systems include Expression Thermodynamics FLux (ETFL), which efficiently integrates RNA protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable E. coli. adapt this Saccharomyces cerevisiae, we developed yETFL, augmented original formulation additional considerations biomass composition, compartmentalized cellular expression system, energetic costs processes. We demonstrated ability yETFL predict maximum growth rate, essential genes, phenotype overflow metabolism. envision that presented extended wide range eukaryotic benefit academic research.

Language: Английский

Citations

72
Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints DOI Creative Commons
Feiran Li, Yu Chen, Qi Qi

et al.

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

Published: May 27, 2022

Abstract Eukaryotic cells are used as cell factories to produce and secrete multitudes of recombinant pharmaceutical proteins, including several the current top-selling drugs. Due essential role complexity secretory pathway, improvement for protein production through metabolic engineering has traditionally been relatively ad-hoc; a more systematic approach is required generate novel design principles. Here, we present proteome-constrained genome-scale model yeast Saccharomyces cerevisiae (pcSecYeast), which enables us simulate explain phenotypes caused by limited capacity. We further apply pcSecYeast predict overexpression targets proteins. experimentally validate many predicted α-amylase demonstrate application computational tool in guiding improving production.

Language: Английский

Citations

53
Genome-scale metabolic network models: from first-generation to next-generation DOI
Chao Ye,

Xinyu Wei,

Tian‐Qiong Shi

et al.

Applied Microbiology and Biotechnology, Journal Year: 2022, Volume and Issue: 106(13-16), P. 4907 - 4920

Published: July 13, 2022

Language: Английский

Citations

42
Genome-scale modeling of yeast metabolism: retrospectives and perspectives DOI Creative Commons
Yu Chen, Feiran Li, Jens Nielsen

et al.

FEMS Yeast Research, Journal Year: 2022, Volume and Issue: 22(1)

Published: Jan. 1, 2022

Abstract Yeasts have been widely used for production of bread, beer and wine, as well bioethanol, but they also designed cell factories to produce various chemicals, advanced biofuels recombinant proteins. To systematically understand rationally engineer yeast metabolism, genome-scale metabolic models (GEMs) reconstructed the model Saccharomyces cerevisiae nonconventional yeasts. Here, we review historical development GEMs together with their recent applications, including flux prediction, factory design, culture condition optimization multi-yeast comparative analysis. Furthermore, present an emerging effort, namely integration proteome constraints into GEMs, resulting in improved performance. At last, discuss challenges perspectives on constraints.

Language: Английский

Citations

39
Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data DOI Creative Commons
Johan Gustafsson,

Mihail Anton,

Fariba Roshanzamir

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2023, Volume and Issue: 120(6)

Published: Jan. 31, 2023

Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel differences in metabolism across both cell types and states but requires new computational methods. Here, we present a method for generating cell-type-specific from clusters of single-cell RNA-Seq profiles. Specifically, developed estimate minimum number cells required pool obtain stable models, bootstrapping strategy estimating statistical inference, faster version task-driven integrative network inference tissues algorithm context-specific GEMs. In addition, evaluated effect different normalization methods on model topology generated bulk data. We applied our data mouse cortex neurons tumor microenvironment lung cancer cases found that almost every subtype had unique profile. approach was able detect cancer-associated between healthy cells, showcasing its utility. also contextualized 202 19 human organs using Human Protein Atlas made these available web portal Metabolic Atlas, thereby providing valuable resource scientific community. With ever-increasing availability datasets continuously improved GEMs, their combination holds promise become an important study metabolism.

Language: Английский

Citations

31
Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects DOI Creative Commons

Zhijin Gong,

Jiayao Chen, Xinyu Jiao

et al.

Biotechnology Advances, Journal Year: 2024, Volume and Issue: 72, P. 108319 - 108319

Published: Jan. 26, 2024

Language: Английский

Citations

14
DNP-assisted solid-state NMR enables detection of proteins at nanomolar concentrations in fully protonated cellular milieu DOI
Whitney N. Costello, Yiling Xiao, Frédéric Mentink‐Vigier

et al.

Journal of Biomolecular NMR, Journal Year: 2024, Volume and Issue: 78(2), P. 95 - 108

Published: March 23, 2024

Language: Английский

Citations

9
Engineered yeast for efficient de novo synthesis of 7‐dehydrocholesterol DOI

Lisha Qu,

Xiang Xiu,

Guoyun Sun

et al.

Biotechnology and Bioengineering, Journal Year: 2022, Volume and Issue: 119(5), P. 1278 - 1289

Published: Feb. 7, 2022

The synthesis of vitamin D3 precursor 7-dehydrocholesterol (7-DHC) by microbial fermentation has much attracted attention owing to its advantages environmental protection. In this study, Saccharomyces cerevisiae was engineered for a de novo biosynthesis 7-DHC. First, seven essential genes (six endogenous and one heterologous gene) were overexpressed, the ROX1 gene (heme-dependent repressor hypoxic genes) knocked out. resulting strain produced 82.6 mg/L 7-DHC from glucose. Then, we predicted five knockout targets overproduction reconstruction genome-scale metabolic model. GDH1 increased titer 101.5 mg/L, specific growth rate ΔGDH1 mutant also 28%. Next, Ty1 transposon in S. applied increase copies ERG1 DHCR24 gene, 120% 223.3 mg/L. Besides, optimize flux distribution, Clustered Regularly Interspaced Short Palindromic Repeats interference (CRISPRi) system used dynamically inhibit competitive pathway, best binding site ERG6 (delta (24)-sterol C-methyltransferase) promoter screened OD600 value regulated cells 43% than knocking out directly, 365.5 shake flask. Finally, reached 1328 3-L bioreactor up 114.7 mg/g dry cell weight). Overall, study constructed yeast chassis highly efficient production systems engineering.

Language: Английский

Citations

31