How many transcription factors in yeast

GIP1 for all regulator mutants, not just those in which mRNA levels are significantly affected data not shown. Moreover, the P -value and log 2 ratio of the change in mRNA levels of the target gene for each regulator mutant is included in the resulting output as a downloadable table. A, B Analysis and visualization of regulators that control expression of the GIP1 gene, a sporulation-specific regulator of the Glc7 phosphatase.

Each regulator is grouped by protein complex membership or functional category. For simplicity, only regulator mutant profiles from yeast grown in SC media conditions are depicted. Blue arrows represent positive regulation; red lines with a cross bar represent negative regulation. Black undirected edges between regulators indicate a shared functional category or complex. C, D Analysis of regulators that control the expression of the key ribonucleotide reductase gene RNR1.

Asterisks indicate regulators that directly bind to the promoter or coding region of the RNR1 gene based on published ChIP-chip data.

Same as in part B , except solid lines indicate the regulator binds to target gene; dashed lines indicate the target gene is not bound or binding data is not available. Importantly, regulatory relationships between regulators are also depicted.

Other complicated regulatory relationships involving Opi3, Ctk1, etc. The Target Viewer and Regulator Network tools also integrate ChIP-chip DNA binding data in the gene expression network in order to indicate which regulatory relationships involve direct DNA-binding of the regulator to the promoter or coding region of the target gene.

ChIP-chip data indicate that promoter or coding region of the target gene is bound by the regulator , while dashed lines indicate targets that are not bound or in which DNA binding data are not available.

Inspection of the resulting network indicates, for example, that the regulation by the Mbp1 and Gln3 transcription factors is likely direct because they directly bind the RNR1 target gene Figure 1D. Moreover, it is apparent that the Rad6 E2 ubiquitin conjugating enzyme may affect the expression of RNR1 indirectly by regulating the expression of Gln3 and Dun1 Figure 1D.

In addition to analyzing the regulation of individual genes, RegulatorDB can be used to identify regulators that coordinately control the expression of genes within co-expressed or functional gene sets. The effects of Rpn4 and other regulators on the expression of individual target genes can be visualized using the Regulator Cluster tool, which can be directly accessed from the results page of the Gene Set Overlap tool output.

This particular visualization displays and clusters the genes based on whether they are differentially expressed either up- or down-regulated in each regulator mutant. Inspection of the clustering data indicate that the genes encoding the Rpn13, Rpn1 and Rpn2 proteasome subunits are differentially expressed in a relatively large number of regulator mutants relative to other proteasome subunits Figure 2B. The Regulator Cluster tool can also cluster target genes based on the actual log ratio of the change in mRNA levels in each regulator mutant Figure 2C.

Analysis of the regulation of proteasome genes 33 genes using the Gene Set Overlap and Regulator Cluster tools. A Overlap of up- or down-regulated target genes for each regulator in the transcription factor yeast grown in YPD category with the proteasome gene set. Only regulators with targets in this gene set are depicted. B Differential expression clustering, in which the gene expression changes are represented as up-regulated, down-regulated, or unchanged in each regulator mutant for genes in the proteasome gene set.

C Log ratio clustering using the Regulator Cluster tool, in which the clustering is based on the log 2 mRNA ratio of each gene in each regulator mutant. Figure 3 shows an example of the box plot display of the expression changes of the eight core histone genes for the chromatin regulator category, in this case depicting only regulators in which the expression of the histone genes was significantly altered in the regulator mutant.

Visualization of the expression changes of the histone genes in all chromatin regulator mutant profiles is shown in Figure 3. This analysis identified many known regulators of histone gene expression, such as the HIR complex [Hir1, Hir2, Hir3 and Hpc2 22 ], as well as number of potential novel regulators of histone expression.

These include a number of factors involved in chromatin assembly, such as subunits of the chromatin assembly factor-I CAF-I complex and Rtt histone acetyltransferase Figure 3. It is possible that histone gene transcription is reduced in these mutants due to their defects in chromatin assembly, in order to avoid the accumulation of excess free histones, which can induce genome instability and is generally toxic to cells 23 , Importantly, the Gene Set Viewer tool uses a sensitive method the non-parametric Wilcoxon Rank Sum test to detect significant associations with regulators, and thus can detect regulator-target gene associations that are relatively subtle or small in magnitude but are consistent across a set of co-regulated genes.

For example, many of the changes in histone gene expression in these regulator mutants did not meet the typical threshold for significance [i. A box plot depicting the log 2 mRNA ratios of the histone genes for each regulator mutant in the chromatin regulator category is depicted. Only regulator mutants that significantly affect the expression of the histone gene set calculated using Wilcoxon rank sum test, see methods are displayed.

Regulators are grouped by protein complex or functional category. Target genes were identified using the Regulator Targets tool, and the depicted network display is adapted from the output of this tool.

The Regulator Targets tool displays all of the target genes whose expression is significantly affected by a user-selected regulator or set of regulators. Again, the user can define the P -value and fold change threshold used for target gene identification.

The resulting output Figure 3B revealed many known targets, including most of the canonical histone genes i. In summary, we anticipate that the RegulatorDB database will have significant utility for elucidating the regulation of individual genes, gene sets and genetic pathways in the widely used model eukaryote S. Importantly, by integrating DNA binding data and mutant expression profiles in a user-friendly manner, the RegulatorDB analysis tools could greatly facilitate the study of transcriptional regulatory networks in this important model organism.

The cell growth OD of the deletion mutants and transformants was measured after 30 and h of incubation Table 1. After 30 h of incubation, the control transformant BYPB did not exhibit more resistance than the host strain BY Thus, as expected, transformation with the plasmid backbone did not lead to elevated resistance to inhibitors.

M-Y and M-S were more sensitive than the BY control to coniferyl aldehyde, furfural, HMF and to the two pretreatment liquids after 30 h incubation. After h incubation, M-Y did not grow in coniferyl aldehyde, and was more sensitive than M-S, which grew to the relatively high OD of 0. After 30 h incubation, M-S was more sensitive to furfural than M-Y, but the two deletion mutants grew to a similar level after h incubation.

That indicated that YAP1 could only partially compensate for STB5 with regard to resistance against the inhibitors and the pretreatment liquids. T-Y was also more resistant than YIS in all the cases after 30 h incubation. The results indicated that even though either of YAP1 or STB5 was overexpressed, the deletion of the other TF of those two TFs would impair the resistance of yeast to the inhibitors and pretreatment liquids.

Cell growth, glucose consumption, ethanol production and cell viability were measured during the fermentation. BY, the deletion mutants, and the transformants grew similarly in culture medium without inhibitors control medium. After 8 h cultivation, all mutants and transformants had entered the exponential phase, and after 16 h they had entered the stationary phase.

The resistance of the yeast cells to 1. All five mutants and transformants were still in the lag phase after 16 h cultivation Fig. BY started to grow between 16 and 32 h, and reached the stationary phase before 32 h.

In accordance with this, the glucose was almost depleted by BY between 16 and 32 h Fig. YIS started to grow after 32 h cultivation, and reached the stationary phase before 64 h. In agreement with that, YIS consumed almost all the glucose between 32 and 64 h Fig.

M-S entered the exponential phase after 88 h cultivation, and reached the stationary phase before h. The glucose was accordingly consumed during this period. M-Y and SIY could not grow with 1. The resistance of the yeast cells to SIY grew slower than M-Y. Even after h of cultivation, M-S did not grow with Cell growth and glucose consumption during flask experiments. With the control medium, the volumetric ethanol productivity Q 16h of the five mutants and transformants reached about 0.

With coniferyl aldehyde in the culture medium, none of the five mutants and transformants produced any ethanol before 16 h. M-S did not produce any ethanol in the h fermentation with HMF. The cell viability was measured after 8 h of cultivation with coniferyl aldehyde, HMF and the control medium. However, the portion of cells with intact cell membrane was lower in cultures with coniferyl aldehyde than in cultures with control medium, especially with regard to M-Y.

The engineering of microbial strains has been an important technique for production of biofuels and bioproducts [ 14 , 15 , 16 ]. Knowledge of resistance of microorganisms to stress conditions is important for engineering robust microbial strains. Besides the development of novel carbohydrate-utilization pathways and the overexpression of cellulase in S.

A better understanding of the regulation of the resistance of yeast to lignocellulose-derived inhibitors, as provided by this investigation, facilitates characterization and engineering of hyper-resistant strains. Using deletion mutants we screened the involvement of 29 MDR-related TFs with respect to resistance to three model inhibitors, coniferyl aldehyde, furfural, and HMF, and two pretreatment liquids, one from sugarcane bagasse and the other from spruce.

All deletion mutants investigated showed increased sensitivity or increased resistance to at least one compound or pretreatment liquid, indicating that all those MDR-related TFs in some way were involved in the resistance to lignocellulose-derived inhibitors.

As 13 out of 29 TFs gave the same response for all the three model inhibitors as for the two pretreatment liquids, the set of model inhibitors chosen was well connected with the inhibitory effects of complex lignocellulosic hydrolysates.

The results therefore also indicate that the three model inhibitors do not cover all inhibitory effects of the pretreatment liquids, which is expected as there are many other inhibitors that can affect yeast cell growth [ 1 , 19 ].

The discrepancy is probably due to the different approaches taken. We evaluated the involvement of the TFs in the response to the inhibitors through the relative growth rates of deletion mutants and transformants, not through transcriptomics as Ma and Liu [ 20 ]. The approach taken in our study seems more advantageous for finding proteins that are truly important for resistance, as products of genes that are not much induced in microarray analysis studies e.

STB5 might be very important for the adaption to the inhibitor. Furthermore, transcripts differ in stability and their abundance may not directly reflect the abundance of the corresponding proteins. The effect of different TFs on transcription levels may also differ. The RPN4 transcription factor stimulates expression of proteasome genes, and is rapidly degraded by the 26S proteasome [ 21 ].

Disruption of the Rpn4-induced proteasome expression in S. In agreement with that, our result indicates that either the deletion or the over-expression of RPN4 with a potent promoter PGK1 of a multicopy plasmid pAJ was not good for the resistance of the cells to the pretreatment liquids.

The analysis showed that genes belonging to two functional categories as defined by MIPS Functional Categories , C-compound and carbohydrate transport [ Genes in those two functional categories are predicted to be potentially important for the resistance of yeast to the fermentation inhibitors in the pretreatment liquids, and could be further studied with regard to engineering the resistance of yeast to lignocellulose-derived inhibitors.

The deletion mutants of STB5 and YAP1 , the two TFs involved in oxidative stress, were consistently sensitive to all model inhibitors and pretreatment liquids.

Among the 29 deletion mutants that were compared, the deletion mutant of STB5 was most sensitive to the furan aldehydes the relative growth rates in media with furfural and HMF were 0. This result indicates that adaptation of yeast cells to oxidative stress is critical for resistance to lignocellulose-derived inhibitors.

STB5 encodes a zinc transcription factor protein, which is required as a basal regulator of the PPP pentose phosphate pathway in S. YAP1 is the major oxidative stress transcription factor in S. It is involved in stress response, which offers protection against a variety of different forms of stress potentially induced by aldehyde inhibitors through damage of the cell membrane, cell wall and DNA and RNA synthesis [ 9 ].

Moreover, both furfural and HMF deplete cellular glutathione levels and accumulate reactive oxygen species [ 27 ]. YAP1 is involved in the glutathione pathway, and is important in the detoxification of intracellular reactive oxygen species ROS [ 26 ]. The fermentability of the hydrolysates could be improved dramatically by treatment with reducing agents, such as dithionite and sulfite [ 28 ], which could react with aromatic compounds and furan aldehydes in the hydrolysates [ 29 ].

The fermentability of hydrolysates can also be improved by oxidation catalyzed by phenol-oxidizing enzymes such as laccases and peroxidases [ 30 ]. These findings indicate that the oxidation—reduction states of hydrolysates are relevant with regard to their toxicity. With that as background it makes sense that STB5 and YAP1 , the two TFs involved in oxidative stress, are important for the resistance of yeast to lignocellulose-derived inhibitors.

HAA1 and WAR1 are both involved in acid stress adaption [ 31 ], but differed considerably in the screening experiments with the deletion mutants.

HAA1 is the main regulator for the adaption of yeast cells to acetic acid [ 31 ]. WAR1 is the regulator of PDR12, a plasma membrane protein which confers resistance of yeast to lipophilic organic acids [ 32 ].

High concentrations of aliphatic carboxylic acids, such as acetic acid, formic acid, and levulinic acid, and aromatic acids are well known to inhibit fermentations [ 1 , 33 ]. The deletion mutants of CIN5 and MSN2 , which have been found to be involved in osmotic stress [ 34 , 35 ], gave the same pattern, as the furans always gave higher values of relative growth rate than the other inhibitors Fig. The screening results indicate that these two TFs have a similar function in the resistance to lignocellulose-derived inhibitors.

PDR1 is involved in the resistance to multiple drugs of unrelated structure and function, such as cycloheximide, oligomycin, and venturicidin [ 37 ]. Disruption of both PDR1 and PDR3 resulted in high drug sensitivity to cycloheximide, oligomycin, and chloramphenicol, whereas disruption of only PDR3 had a limited or undetectable effect [ 38 ].

Even though PDR1 and PRD3 are homologs to each other [ 38 ], the deletion mutants of these two TFs behaved oppositely with regard to the resistance to the pretreatment liquids. That can explain why the deletion mutant of PDR1 was sensitive to all the inhibitory substances tested in this study, while the deletion mutant of PDR3 was not. YRR1 is required for resistance to 4-nitroquinoline N -oxide [ 40 ]. Our results showed that YRR1 was also required for resistance to the lignocellulose-derived inhibitors and the pretreatment liquids.

This agrees well with our finding that the deletion mutants of YRR1 and YRM1 were sensitive to all the three inhibitors and to the two pretreatment liquids. Among the TFs involved in the regulation of metabolism, including carbon source responsive, amino acid biosynthesis and nitrogen catabolism, LEU3 and DAL81 behaved similarly. LEU3 participates in the transcriptional regulation of the branched-chain amino-acid biosynthetic pathways [ 42 ]. DAL81 is a positive regulator of genes in multiple nitrogen degradation pathways [ 43 ].

Our results indicate that amino-acid biosynthesis and nitrogen degradation pathways are of importance for yeast in the resistance to the pretreatment liquids. The deletion of ECM22 and UPC2 might aggravate adverse effects on the sterol biosynthesis when the cells were cultivated with furan aldehydes.

Even though phenolics have been found to interfere with the cell membrane [ 46 ], the deletion mutants of ECM22 and UPC2 were not sensitive to coniferyl aldehyde. That may because the concentration of coniferyl aldehyde in the experiments 1. The experiments with permutations of deletion and overexpression of STB5 and YAP1 indicated that the roles of the two TFs were complementary with regard to HMF resistance, in a sense that STB5 and YAP1 could at least in part take over the role of each other when the other TF was lacking, but distinct with regard to coniferyl aldehyde resistance, in a sense that STB5 could not take over the role of YAP1 in the resistance to coniferyl aldehyde when the latter TF was lacking.

They are required for the maintenance of mitochondrial functions, and some of them have been found to be involved in oxidative stress response [ 47 , 48 , 49 ].

The extended role played by Stb5p in the regulation of the PPP compared to that of Yap1p can explain why Stb5p was more important than Yap1p with respect to resistance against furan aldehydes. Flr1p and Atr1p, two MDR proteins that have been found to be involved in the resistance to coniferyl aldehyde [ 6 , 7 ], are positively regulated by Yap1p, but not by Stb5p.

A lot of previous work has been done in S. And in the course of their studies, Wollman and coworkers were able to estimate how many Mig1p molecules were in yeast cells and how many were in clusters.

T his mythical beast is more like a cluster of Mig1p proteins with its multiple arms representing multiple DNA binding domains with which to grab strands of DNA. Wikimedia Commons. They now had all the information they needed to run their simulations.

The final step was to work out what part of Mig1p was involved in forming the clusters. To do this, the authors compared Mig1p and Msn2p, the second transcription factor they studied that also formed into clusters, and looked for structural regions they might have in common.

What they found was both proteins had a highly disordered region. For Mig1p, it was at the C-terminus and for Msn2p it was at the N-terminus.

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