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Related Concept Videos

Yeast Signaling01:28

Yeast Signaling

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Yeasts are single-celled organisms, but unlike bacteria, they are eukaryotes (cells with a nucleus). Cell signaling in yeast is similar to signaling in other eukaryotic cells. A ligand, such as a protein or a small molecule released from a yeast cell, attaches to a receptor on the cell surface. The binding stimulates second-messenger kinases to activate or inactivate transcription factors that further regulate gene expression. Many of the yeast intracellular signaling cascades have similar...
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Ribosome Profiling02:24

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
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Related Experiment Video

Updated: Jun 5, 2025

High Throughput Yeast Strain Phenotyping with Droplet-Based RNA Sequencing
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Oleaginous Yeast Biology Elucidated With Comparative Transcriptomics.

Sarah J Weintraub1, Zekun Li2, Carter L Nakagawa1

  • 1Department of Bioinformatics and Computational Biology, Worcester Polytechnic Institute, Worcester, Massachusetts, USA.

Biotechnology and Bioengineering
|December 11, 2024
PubMed
Summary

This study links yeast genotype to phenotype for biomanufacturing applications. Complementary computational methods reveal how yeast strains respond to stress, improving our understanding of their genetic makeup and beneficial traits.

Keywords:
metabolic networkoleaginoustolerancetranscriptomicsunsupervised machine learningyeast

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Area of Science:

  • Microbiology
  • Systems Biology
  • Biotechnology

Background:

  • Extremophilic yeasts possess valuable traits for biomanufacturing, including lipid biosynthesis, flavinogenesis, and halotolerance.
  • The genetic underpinnings of these advantageous phenotypes in yeast are not fully understood, hindering their biotechnological application.

Purpose of the Study:

  • To investigate the connection between genotype and phenotype in biotechnologically relevant yeasts.
  • To compare the phenotypic and gene expression responses of Yarrowia lipolytica, Debaryomyces hansenii, and Debaryomyces subglobosus under various stress conditions.

Main Methods:

  • Utilized a "network-first" approach mapping genes onto a generalized metabolic network.
  • Employed a "cluster-first" approach using unsupervised machine learning for gene co-expression analysis.
  • Analyzed transcriptomics data across species and conditions, including nitrogen starvation, iron starvation, and salt stress.

Main Results:

  • Confirmed Yarrowia upregulates lipid biosynthesis under nitrogen starvation.
  • Provided evidence that riboflavin overproduction in Debaryomyces is overflow metabolism under salt stress.
  • Identified known and novel genes, including transcription factors and transporters, involved in stress responses using co-expression analysis.

Conclusions:

  • Successfully linked genotype to phenotype in biotechnologically relevant yeasts.
  • Demonstrated the utility of complementary computational approaches for analyzing large-scale transcriptomics data.
  • Enhanced understanding of yeast stress responses and metabolic capabilities for biomanufacturing optimization.