Reconstruction of a Global Transcriptional Regulatory Network for Control of Lipid Metabolism in Yeast by Using Chromatin Immunoprecipitation with Lambda Exonuclease Digestion

Bergenholm, David; Liu, Guodong; Holland, Petter; Nielsen, Jens

doi:10.1128/msystems.00215-17

Cited by 32 publications

(41 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the performance of the MCs further, we performed transcriptome analysis using RNAseq of one Hap1‐tagged strain (initially constructed for a ChIP‐exo study (Bergenholm, Liu, Holland, & Nielsen, )) in both the DASGIP system and in the MC. We also used one WT strain in the MC as a reference to the tagged strain.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Construction of mini‐chemostats for high‐throughput strain characterization

Bergenholm

Liu

Hansson

et al. 2019

Biotech & Bioengineering

Self Cite

View full text Add to dashboard Cite

To achieve large‐scale, high‐throughput experiments for systems biology research of microorganisms, reliable data from robust cultivation systems are needed. Chemostats are such systems, ensuring reproducibility and quality by providing a stable, well‐controlled environment for the cells. However, many of the available chemostat systems require large amounts of media and are complex to set up and expensive to purchase and maintain. To address these concerns, we developed a mini‐chemostat (MC) system with 16 reactors, each at a working volume of 40 ml. Sensors measure dissolved oxygen in the reactor, while OD600 is measured in the outflow. We further developed a CO2 and pH sensor array that can be plugged into the outflow of the reactors. The system was used to characterize yeast physiology at four metabolically different conditions: limitations of glucose, both aerobic and anaerobic, nitrogen, and ethanol. The physiology of yeast cells grown at the four different conditions in the MC system was compared with the yeast cells grown in a DASGIP 1 L system using RNAseq analysis. The results show that the MC system provides the same environmental conditions as the DASGIP system and that the MC system is reproducible between different runs. The system is built to be easily scalable with more reactors and to include more sensors, if available. Our study shows that a robust, reproducible chemostat system for high‐throughput and large‐scale experiments can be built at low costs.

show abstract

Section: Resultsmentioning

confidence: 99%

“…2 hr. dO 2 : dissolved oxygen; MC: mini-chemostat; OD: optical density constructed for a ChIP-exo study (Bergenholm, Liu, Holland, & Nielsen, 2018)) in both the DASGIP system and in the MC. We also used one WT strain in the MC as a reference to the tagged strain.…”

Section: Transcriptome Comparisonmentioning

confidence: 99%

Construction of mini‐chemostats for high‐throughput strain characterization

Bergenholm

Liu

Hansson

et al. 2019

Biotech & Bioengineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Oaf1 transcription factor binds throughout the OLE1 promoter including in the close vicinity of the FAR variant [64]. This raises the possibility that the effects of OAF1 L63S and the FAR variant may interact with each other genetically.…”

Section: Resultsmentioning

confidence: 99%

DNA variants affecting the expression of numerous genes intranshave diverse mechanisms of action and evolutionary histories

Lutz

Brion

Kliebhan

et al. 2019

Preprint

View full text Add to dashboard Cite

12 DNA variants that alter gene expression contribute to variation in many phenotypic traits. In 13 particular, trans-acting variants, which are often located on different chromosomes from the genes 14 they affect, are an important source of heritable gene expression variation. However, our 15 knowledge about the identity and mechanism of causal trans-acting variants remains limited. Here, 16 we developed a fine-mapping strategy called CRISPR-Swap and dissected three expression 17 quantitative trait locus (eQTL) hotspots known to alter the expression of numerous genes in trans 18 in the yeast Saccharomyces cerevisiae. Causal variants were identified by engineering 19 recombinant alleles and quantifying the effects of these alleles on the expression of a green 20 fluorescent protein-tagged gene affected by the given locus in trans. We validated the effect of 21 each variant on the expression of multiple genes by RNA-sequencing. The three variants were 22 strikingly different in their molecular mechanism, the type of genes they reside in, and their 23 distribution in natural populations. While a missense leucine-to-serine variant at position 63 in the 24 transcription factor Oaf1 (L63S) was almost exclusively present in the reference laboratory strain, 25 the two other variants were frequent among S. cerevisiae isolates. A causal missense variant in the 26 glucose receptor Rgt2 (V539I) occurred at a poorly conserved amino acid residue and its effect 27 was strongly dependent on the concentration of glucose in the culture medium. A noncoding 28 variant in the conserved fatty acid regulated (FAR) element of the OLE1 promoter influenced the 2 29 expression of the fatty acid desaturase Ole1 in cis and, by modulating the level of this essential 30 enzyme, other genes in trans. The OAF1 and OLE1 variants showed a non-additive genetic 31 interaction, and affected cellular lipid metabolism. These results revealed remarkable diversity in 32 the molecular basis of trans-regulatory variation, highlighting the challenges in predicting which 33 natural genetic variants affect gene expression. 34Author summary 35 Differences in the DNA sequence of individual genomes contribute to differences in many traits, 36 such as appearance, physiology, and the risk for common diseases. An important group of these 37 DNA variants influences how individual genes across the genome are turned on or off. In this 38 paper, we describe a strategy for identifying such "trans-acting" variants in different strains of 39 baker's yeast. We used this strategy to reveal three single DNA base changes that each influences 40 the expression of dozens of genes. These three DNA variants were very different from each other. 41 Two of them changed the protein sequence, one in a transcription factor and the other in a sugar 42 sensor. The third changed the expression of an enzyme, a change that in turn caused other genes 43 to alter their expression. One variant existed in only a few yeast isolates, while the other two 44 existed in many isolates collected fro...

show abstract

“…[60] Using the higher-resolution ChIP-exo, combined with the characterization of binding at four different conditions, it was possible to identify a large number of new binding targets as well as identify extensive binding overlap events for several different TFs on the same promoters, thus pointing to there being far more complex transcriptional regulation in yeast than previously reported. [60] Thus, to identify causal connectivity between TF binding determined by ChIP technologies and transcriptional regulation, it is imperative to generate data for many more conditions, and possibly including data for a larger number of TFs. Nonetheless, with this kind of data at hand, it may be possible to use machine learning for developing models that describe the role of TFs in gene expression control.…”

Section: Chemical Class Chemical Application Referencesmentioning

confidence: 96%

“…Recently, we implemented ChIP‐exo for more precise mapping of TF binding and used this to identify the binding of TFs involved in the regulation of lipid metabolism under four different environmental conditions . Using the higher‐resolution ChIP‐exo, combined with the characterization of binding at four different conditions, it was possible to identify a large number of new binding targets as well as identify extensive binding overlap events for several different TFs on the same promoters, thus pointing to there being far more complex transcriptional regulation in yeast than previously reported . Thus, to identify causal connectivity between TF binding determined by ChIP technologies and transcriptional regulation, it is imperative to generate data for many more conditions, and possibly including data for a larger number of TFs.…”

Section: Systems Biologymentioning

confidence: 99%

Yeast Systems Biology: Model Organism and Cell Factory

Nielsen

2019

Biotechnology Journal

Self Cite

203

109

View full text Add to dashboard Cite

For thousands of years, the yeast Saccharomyces cerevisiae (S. cerevisiae) has served as a cell factory for the production of bread, beer, and wine. In more recent years, this yeast has also served as a cell factory for producing many different fuels, chemicals, food ingredients, and pharmaceuticals. S. cerevisiae, however, has also served as a very important model organism for studying eukaryal biology, and even today many new discoveries, important for the treatment of human diseases, are made using this yeast as a model organism. Here a brief review of the use of S. cerevisiae as a model organism for studying eukaryal biology, its use as a cell factory, and how advances in systems biology underpin developments in both these areas, is provided.

show abstract

Reconstruction of a Global Transcriptional Regulatory Network for Control of Lipid Metabolism in Yeast by Using Chromatin Immunoprecipitation with Lambda Exonuclease Digestion

Cited by 32 publications

References 55 publications

Construction of mini‐chemostats for high‐throughput strain characterization

Construction of mini‐chemostats for high‐throughput strain characterization

DNA variants affecting the expression of numerous genes intranshave diverse mechanisms of action and evolutionary histories

Yeast Systems Biology: Model Organism and Cell Factory

Contact Info

Product

Resources

About