Feedback control of stochastic noise in the yeast galactose utilization pathway

Orrell, David; Ramsey, Stephen A.; Marelli, Marcello; Smith, Jennifer J.; Petersen, Timothy W.; Atauri, Pedro de; Aitchison, John D.; Bolouri, Hamid

doi:10.1016/j.physd.2006.03.010

Cited by 10 publications

(9 citation statements)

References 41 publications

(41 reference statements)

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“…We speculate that acting alone, Oaf1p-Pip2p-mediated expression is noisy because PIP2 mRNA has a high level of fluctuations in the model (as defined by the steady-state CV), and this variability is presumably due to the low copy number of PIP2 mRNA (see Data S1) and the fact that PIP2 is positively autoregulated. Previous studies have established that a low copy number of a gene's mRNA (34,35) and positive autoregulation of a gene (36) can both contribute to variations in the protein level, and, in the case of a TF, to increased variability of expression of downstream gene targets (extrinsic noise) (37). In the presence of Adr1p, this noise is expected to be buffered because of its direct regulatory influence on the target gene, which increases AOPY gene expression, thereby decreasing the relative variation in expression from the target.…”

Section: Discussionmentioning

confidence: 99%

Control of Transcriptional Variability by Overlapping Feed-Forward Regulatory Motifs

Ratushny¹,

Ramsey²,

Roda

et al. 2008

Biophysical Journal

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In yeast, beta-oxidation of fatty acids (FAs) takes place in the peroxisome, an organelle whose size and number are controlled in response to environmental cues. The expression of genes required for peroxisome assembly and function is controlled by a transcriptional regulatory network that is induced by FAs such as oleate. The core FA-responsive transcriptional network consists of carbon source-sensing transcription factors that regulate key target genes through an overlapping feed-forward network motif (OFFNM). However, a systems-level understanding of the function of this network architecture in regulating dynamic FA-induced gene expression is lacking. The specific role of the OFFNM in regulating the dynamic and cell-population transcriptional response to oleate was investigated using a kinetic model comprised of four core transcription factor genes (ADR1, OAF1, PIP2, and OAF3) and two reporter genes (CTA1 and POT1) that are indicative of peroxisome induction. Simulations of the model suggest that 1), the intrinsic Adr1p-driven feed-forward loop reduces the steady-state expression variability of target genes; 2), the parallel Oaf3p-driven inhibitory feed-forward loop modulates the dynamic response of target genes to a transiently varying oleate concentration; and 3), heterodimerization of Oaf1p and Pip2p does not appear to have a noise-reducing function in the context of oleate-dependent expression of target genes. The OFFNM is highly overrepresented in the yeast regulome, suggesting that the specific functions described for the OFFNM, or other properties of this motif, provide a selective advantage.

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Section: Discussionmentioning

confidence: 99%

Control of Transcriptional Variability by Overlapping Feed-Forward Regulatory Motifs

Ratushny¹,

Ramsey²,

Roda

et al. 2008

Biophysical Journal

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“…Distributions of these two enzyme activities in the foliage provide state-dependent enzyme correlation as the consequence of the leaf sampling from various branches and places but in the same time. Distributions of these enzyme activities have been checked by Kolmogorov-Smirnov test [7]. This test did not contradict the assumption that these data might follow normal distribution.…”

Section: State-dependent Correlations Of Some Biochemical Variables Imentioning

confidence: 99%

“…To minimize the stochastic noise of gene expression there are specific mechanisms in the cell and this process in the yeast galactose utilization pathway have already been modelled [7]. Moreover, the biological noise has been established to have a regulatory role in the gene expression [8] too.…”

Section: Ranks Of the Values Of Variables {Rmentioning

confidence: 99%

State‐dependent correlations of biochemical variables in plants

Németh

Sárdi

Stefanovits-Bányai

2009

Journal of Chemometrics

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Distributions of biochemical variables in plant foliage are mainly similar to normal distributions. The correlations between two variables belonging to a given sampling time or a given physiological stage are regarded as state-dependent correlations. The conditions of existence of strong state-dependent correlation are the dependence of values of the variables from each other and the identity of their distributions. An assumed identity of distributions can manifest itself in the very strong similarity of their empirical distributions. Two distributions with finite number of elements are considered the same in their type when they approximate a characteristic distribution, for example, the normal distribution in the same manner. In this situation, the rank or normal score patterns of the data producing linear correlation are also very similar to each other. On the basis of identity criteria and the interdependence of the variables, an equation for state-dependent correlation has been derived in theoretical way from standardization of the distributions. Thus, not only statistical but also physical meanings can be attached to the parameters of the regression of experimental results. In this paper, we show that the distinction of plant physiological and/or stress states from each other can be more effective by using the correlation of biochemical variables for their characterizations than by separately statistical comparisons of means of the variables. Covariance analysis (ANCOVA) of linear regressions of state-dependent correlations is able to distinguish actually various states that, on the basis of the means, seem undistinguishable.

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“…However, given the nature of these alleles, it is not known if these polymorphisms are present in wild populations or are laboratory derived. Additional evidence comes from the study of the S. cerevisiae galactose regulon where it was found that genetic manipulation of the regulatory feedback loop could lead to increased stochastic noise in the network's output [30] , [31] . Genetic control of stochastic noise has also been identified using QTLs for yield stability in crops [32] and gene expression in 18 isogenic mouse lines [19] .…”

Section: Introductionmentioning

confidence: 99%

Genomic Analysis of QTLs and Genes Altering Natural Variation in Stochastic Noise

et al. 2011

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Quantitative genetic analysis has long been used to study how natural variation of genotype can influence an organism's phenotype. While most studies have focused on genetic determinants of phenotypic average, it is rapidly becoming understood that stochastic noise is genetically determined. However, it is not known how many traits display genetic control of stochastic noise nor how broadly these stochastic loci are distributed within the genome. Understanding these questions is critical to our understanding of quantitative traits and how they relate to the underlying causal loci, especially since stochastic noise may be directly influenced by underlying changes in the wiring of regulatory networks. We identified QTLs controlling natural variation in stochastic noise of glucosinolates, plant defense metabolites, as well as QTLs for stochastic noise of related transcripts. These loci included stochastic noise QTLs unique for either transcript or metabolite variation. Validation of these loci showed that genetic polymorphism within the regulatory network alters stochastic noise independent of effects on corresponding average levels. We examined this phenomenon more globally, using transcriptomic datasets, and found that the Arabidopsis transcriptome exhibits significant, heritable differences in stochastic noise. Further analysis allowed us to identify QTLs that control genomic stochastic noise. Some genomic QTL were in common with those altering average transcript abundance, while others were unique to stochastic noise. Using a single isogenic population, we confirmed that natural variation at ELF3 alters stochastic noise in the circadian clock and metabolism. Since polymorphisms controlling stochastic noise in genomic phenotypes exist within wild germplasm for naturally selected phenotypes, this suggests that analysis of Arabidopsis evolution should account for genetic control of stochastic variance and average phenotypes. It remains to be determined if natural genetic variation controlling stochasticity is equally distributed across the genomes of other multi-cellular eukaryotes.

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Feedback control of stochastic noise in the yeast galactose utilization pathway

Cited by 10 publications

References 41 publications

Control of Transcriptional Variability by Overlapping Feed-Forward Regulatory Motifs

Control of Transcriptional Variability by Overlapping Feed-Forward Regulatory Motifs

State‐dependent correlations of biochemical variables in plants

Genomic Analysis of QTLs and Genes Altering Natural Variation in Stochastic Noise

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