Molecular characterization of Candida boidinii MIG1 and its role in the regulation of methanol-inducible gene expression

Yeast ◽  
2012 ◽  
Vol 29 (7) ◽  
pp. 293-301 ◽  
Author(s):  
Zhenyu Zhai ◽  
Hiroya Yurimoto ◽  
Yasuyoshi Sakai
2015 ◽  
Vol 14 (3) ◽  
pp. 278-285 ◽  
Author(s):  
Saori Oda ◽  
Hiroya Yurimoto ◽  
Nobuhisa Nitta ◽  
Yu Sasano ◽  
Yasuyoshi Sakai

ABSTRACT We identified genes encoding components of the Hap complex, CbHAP2 , CbHAP3 , and CbHAP5 , as transcription factors regulating methanol-inducible gene expression in the methylotrophic yeast Candida boidinii . We found that the Cbhap2 Δ, Cbhap3 Δ, and Cbhap5 Δ gene-disrupted strains showed severe growth defects on methanol but not on glucose and nonfermentable carbon sources such as ethanol and glycerol. In these disruptants, the transcriptional activities of methanol-inducible promoters were significantly decreased compared to those of the wild-type strain, indicating that CbHap2p, CbHap3p, and CbHap5p play indispensable roles in methanol-inducible gene expression. Further molecular and biochemical analyses demonstrated that CbHap2p, CbHap3p, and CbHap5p localized to the nucleus and bound to the promoter regions of methanol-inducible genes regardless of the carbon source, and heterotrimer formation was suggested to be necessary for binding to DNA. Unexpectedly, distinct from Saccharomyces cerevisiae , the Hap complex functioned in methanol-specific induction rather than glucose derepression in C. boidinii . Our results shed light on a novel function of the Hap complex in methanol-inducible gene expression in methylotrophic yeasts.


2015 ◽  
Vol 112 (26) ◽  
pp. 8148-8153 ◽  
Author(s):  
Jakob Ruess ◽  
Francesca Parise ◽  
Andreas Milias-Argeitis ◽  
Mustafa Khammash ◽  
John Lygeros

Systems biology rests on the idea that biological complexity can be better unraveled through the interplay of modeling and experimentation. However, the success of this approach depends critically on the informativeness of the chosen experiments, which is usually unknown a priori. Here, we propose a systematic scheme based on iterations of optimal experiment design, flow cytometry experiments, and Bayesian parameter inference to guide the discovery process in the case of stochastic biochemical reaction networks. To illustrate the benefit of our methodology, we apply it to the characterization of an engineered light-inducible gene expression circuit in yeast and compare the performance of the resulting model with models identified from nonoptimal experiments. In particular, we compare the parameter posterior distributions and the precision to which the outcome of future experiments can be predicted. Moreover, we illustrate how the identified stochastic model can be used to determine light induction patterns that make either the average amount of protein or the variability in a population of cells follow a desired profile. Our results show that optimal experiment design allows one to derive models that are accurate enough to precisely predict and regulate the protein expression in heterogeneous cell populations over extended periods of time.


Yeast ◽  
2020 ◽  
Author(s):  
Shinya Takahata ◽  
Takahiro Asanuma ◽  
Miyuki Mori ◽  
Yota Murakami

2011 ◽  
Vol 77 (23) ◽  
pp. 8439-8441 ◽  
Author(s):  
Hirofumi Nariya ◽  
Shigeru Miyata ◽  
Tomomi Kuwahara ◽  
Akinobu Okabe

ABSTRACTA xylose-inducible gene expression vector forClostridium perfringenswas developed. Plasmid pXCH contains a chromosomal region fromClostridium difficile(xylR-PxylB):xylR, encoding the xylose repressor,xylO, thexyloperator sequence, and PxylB, the divergent promoter upstream ofxylBAencoding xylulo kinase and xylose isomerase. pXCH allows tightly regulated expression of the chloramphenicol acetyltransferase reporter and the α-toxin genes in response to the inducer concentration. Thus, pXCH could constitute a new valuable genetic tool for study ofC. perfringens.


Sign in / Sign up

Export Citation Format

Share Document