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During stress, L. A comparison of ESR between S.

Environmental Stressors and Gene Responses, Volume 1 - 1st Edition

However, despite the extremely high genetic divergence between these two species, they both show respirofermentative behavior. Therefore it appears very likely that differences in lifestyle are responsible for our observations. However, in L. The action of PKA, which participates in the control of the stress response in S. Because L. Although they are highly expressed, neither of these isoforms is in the positive ESR in L.

Molecular mechanisms regulating lifespan and environmental stress responses

Therefore some PKA targets are also not part of the L. However, most of the PKA controls occur at the posttranslational level, and the full implications of the expression behavior of TPK2 and TPK3 could not be fully addressed here. However, our results indicate that the regulatory mechanisms underlying stress response tend to be different among species. The more pronounced differences in the sets of genes in the ESR across species are mostly found in the positive ESR, whereas the negative ESR displays only few changes.

Consequently, these negative ESR genes are highly conserved within and across species, and their expression is weakly influenced by genetic background, whereas the positive ESR genes have lower levels of expression conservation. The high genetic plasticity of positive ESR gene expression observed in L. Therefore we hypothesized that the exact set of positive ESR genes might change within a species, whereas the negative ESR genes might be mostly identical among strains. Of great interest, previously uncharacterized L. In a small set of follow-up experiments, nearly half of our predictions were validated.

This method has limits: we can propose GO terms only for genes that are involved in large enough networks small networks being subject to noise and generally displaying low enrichment p value. Moreover, based on RNA abundance data alone, this approach does not ensure that the proteins are active in the cell. Consequently, deletion of genes we predicted to be involved in respiration had no effect on fitness on nonfermentable carbon sources. However, the method used here is advantageous because it is easily implemented and can be applied to large sets of expression data.

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The functional validation of three of the associations we predicted between genes and potential pathways indicates that this type of approach can be used, to some extent, to uncover new gene functions. The improvement of gene annotation in L. Moreover, the proposal of gene function through a method other than homology can highlight new differences among species and provide a path to unraveling the evolution of gene function.

Providing a large data set on gene expression for a species distantly related to the well-studied S. First, by completing the description of L. Second, patterns of gene expression allowed us to propose putative involvement in processes for genes for which no orthologue had previously been functionally described. The results obtained here are useful for further genetic investigation of L. The expression analyses were performed using the L. Transcriptomic profiling was completed in mid exponential growth in batch cultures for 20 media listed in Supplemental Table S1. For each condition, biological replicates of two independent cultures were done.

The definition of the stress conditions e. The plate was shaken for 10 min between absorbance recordings at nm for 48 h. Cells were sampled by filtering 7 ml of medium from the mid exponential growth phase at an OD between 0. The alignment rate ranged from We used the most recent annotation file of L. Raw data are available from the European Nucleotide Archive www. The average values of gene expression between replicates were used for all subsequent analyses.

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  4. The final data set is given in Supplemental Table S4. We also calculated log 2 FC for the expression of each gene in the 19 stress-inducing media compared with that in the reference medium YPD.

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    The identification of genes included in significantly distinct clusters was performed using standardized fold-change log 2 FC , that is, SD corrected. An initial HC using all genes with the R hclust function Euclidean distance, complete linkage, stats package was done. For each cluster, the height or distance was measured as the maximum distance across all elements. We performed permutations of expression levels across conditions for all genes, followed by a new HC repeated 10 times in order to define a height threshold of 2. The principle of the method is described in Figure 2B.

    We obtained the list of S. We considered the positive and negative ESR gene lists separately. We considered the remaining genes to be the common set of ESR shared between L. We calculated the pairwise Spearman correlation of expression for all of the genes in L. These partial overlaps were expected, as major changes in expression could also be due to condition-specific stress responses. Functional annotation of the L.


    If no orthologue was detected in S. From the list of orthologue pairs between L. The orthologue identity level was calculated as a factor between the percentage identity with the aligned sequences and the ratio of the aligned sequences to the total protein size. From that list, reliable S.

    By performing permutations of expression levels across conditions for each of the genes before correlation calculation, we determined that only 2. From the list of orthologues, we performed a GO term enrichment using topGO of the R Bioconductor package, with the classic algorithm and Fisher statistical test Alexa and Rahnenfuhrer, with org.

    The three GO terms with the lowest p values were obtained and assigned to the initial gene and were generally involved in related pathways. When the initial gene had a defined orthologue in S. To do so, we used GOsim from the R Bioconductor package, with the relevance method and using org.

    Similarity scores ranged between 0 and 1, and we arbitrarily chose a threshold of 0. We evaluated the reliability of this approach by applying this method to S. For the remaining two-thirds, GOsim occasionally provided a low similarity score for related processes. Because the recombination rate is low in L. For the transformation, cells were harvested at the end of the exponential phase OD of 1. Immediately after electroporation, cells were resuspended in 1 ml of YPD and incubated for 1 h.

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    To stain mitochondria, MitoTracker Green which labels all mitochondria and MitoTracker DeepRed which only labels active mitochondria; Thermo Fisher Scientific, Waltham, MA were added simultaneously to a culture of cells during exponential growth, each at a final concentration of nM.

    After incubation for 1 h, cells were pelleted and resuspended in water. MitoTracker Green was detected using filter set 13 for green fluorescent protein excitation nm, emission — nm and MitoTracker DeepRed with filter set Cy5 excitation nm, emission — nm. Approximately six images were taken randomly for each preparation.

    Image processing was carried out using GIMP2. We thank the two anonymous reviewers for helpful suggestions and comments, as well as Kelle Freel for invaluable advice. E on March 23, National Center for Biotechnology Information , U. Journal List Mol Biol Cell v. Mol Biol Cell. Author information Article notes Copyright and License information Disclaimer. This article is distributed by The American Society for Cell Biology under license from the author s. Two months after publication it is available to the public under an Attribution—Noncommercial—Share Alike 3. This article has been cited by other articles in PMC. Abstract Defining how organisms respond to environmental change has always been an important step toward understanding their adaptive capacity and physiology.

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    Table of Contents

    General environmental stress response in L. Evolution of the ESR genes within L. Functional validation of inferred annotations To functionally validate our approach, we selected seven genes from those with an inferred biological process Supplemental Table S3 that were potentially easy to test by deletion. Differences in ESR between yeast species Using the data set we generated, we focused on the changes in stress responses among species. Conclusion Providing a large data set on gene expression for a species distantly related to the well-studied S.

    Materials and Methods Strain and growth conditions The expression analyses were performed using the L. RNA sampling Cells were sampled by filtering 7 ml of medium from the mid exponential growth phase at an OD between 0. Hierarchical clustering analysis of expression profiles The identification of genes included in significantly distinct clusters was performed using standardized fold-change log 2 FC , that is, SD corrected.