ArticleBiomolecules2024
Integrative Transcriptomic and Target Metabolite Analysis as a New Tool for Designing Metabolic Engineering in Yeast.
Article in Biomolecules, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Closing the Loop with Gates: A Scale-up-Gated Design-Build-Test-Learn Framework for Industrial Fermentation.Microorganisms · 2026Review
- Snapshot at Saccharomycopsis fibuligera metabolism for industrial terpenoid production and bioengineering.BMC microbiology · 2026Article
- From Traditional to Omics-Driven: Emerging Strategies for Isolation, Cultivation, and Identification of Plant Endophytes.Plants (Basel, Switzerland) · 2026Review
- Role of CRISPR in bioremediation of heavy metal(loid): a breakthrough in environmental biotechnology.World journal of microbiology & biotechnology · 2026Review
- High-throughput yeast engineering in biofoundries: towards autonomous and scalable synthetic biology.FEMS yeast research · 2026Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Precision fermentation processes, especially when using edited microorganisms, demand accuracy in the bioengineering process to maximize the desired outcome and to avoid adverse effects. The selection of target sites to edit using CRISPR/Cas9 can be complex, resulting in non-controlled consequences. Therefore, the use of multi-omics strategies can help in the design, selection and efficiency of genetic editing. In this study, we present a multi-omics approach based on targeted metabolite analysis and transcriptomics for the designing of CRISPR/Cas9 in baker's yeast as a more efficient strategy to select editing regions. Multi-omics shows potential to reveal new metabolic bottlenecks and to elucidate new metabolic fluxes, which could be a key factor in minimizing the metabolic burden in edited microorganisms. In our model, we focus our attention on the isoprenoid synthesis due to their industrial interest. Targeted metabolite detection combined with a transcriptomic analysis revealed hydroxymethylglutaryl-CoA reductases (HMGs) as the best target gene to induce an increase in isoprenoid synthesis. Thus, an extra copy of
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Registered trials
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