Jean-Christophe Aude

Sulfur Sparing in the Yeast Proteome in Response to Sulfur Demand

Genome-wide studies have recently revealed the unexpected complexity of the genetic response to apparently simple physiological changes. Here, we show that when yeast cells are exposed to Cd(2+), most of the sulfur assimilated by the cells is converted into glutathione, a thiol-metabolite essential for detoxification. Cells adapt to this vital metabolite requirement by modifying globally their proteome to reduce the production of abundant sulfur-rich proteins. In particular, some abundant glycolytic enzymes are replaced by sulfur-depleted isozymes. This global change in protein expression allows an overall sulfur amino acid saving of 30%. This proteomic adaptation is essentially regulated at the mRNA level. The main transcriptional activator of the sulfate assimilation pathway, Met4p, plays an essential role in this sulfur-sparing response.

Endoplasmic reticulum is a major target of cadmium toxicity in yeast

Cadmium (Cd2+) is a very toxic metal that causes DNA damage, oxidative stress and apoptosis. Despite many studies, the cellular and molecular mechanisms underlying its high toxicity are not clearly understood. We show here that very low doses of Cd2+ cause ER stress in Saccharomyces cerevisiae as evidenced by the induction of the unfolded protein response (UPR) and the splicing of HAC1 mRNA. Furthermore, mutant strains (Δire1 and Δhac1) unable to induce the UPR are hypersensitive to Cd2+, but not to arsenite and mercury. The full functionality of the pathways involved in ER stress response is required for Cd2+ tolerance. The data also suggest that Cd2+‐induced ER stress and Cd2+ toxicity are a direct consequence of Cd2+ accumulation in the ER. Cd2+ does not inhibit disulfide bond formation but perturbs calcium metabolism. In particular, Cd2+ activates the calcium channel Cch1/Mid1, which also contributes to Cd2+ entry into the cell. The results reinforce the interest of using yeast as a cellular model to study toxicity mechanisms in eukaryotic cells.

Genome-wide location analysis reveals a role of TFIIS in RNA polymerase III transcription

TFIIS is a transcription elongation factor that stimulates transcript cleavage activity of arrested RNA polymerase II (Pol II). Recent studies revealed that TFIIS has also a role in Pol II transcription initiation. To improve our understanding of TFIIS function in vivo, we performed genome-wide location analysis of this factor. Under normal growth conditions, TFIIS was detected on Pol II-transcribed genes, and TFIIS occupancy was well correlated with that of Pol II, indicating that TFIIS recruitment is not restricted to NTP-depleted cells. Unexpectedly, TFIIS was also detected on almost all Pol III-transcribed genes. TFIIS and Pol III occupancies correlated well genome-wide on this novel class of targets. In vivo, some dst1 mutants were partly defective in tRNA synthesis and showed a reduced Pol III occupancy at the restrictive temperature. In vitro transcription assays suggested that TFIIS may affect Pol III start site selection. These data provide strong in vivo and in vitro evidence in favor of a role of TFIIS as a general Pol III transcription factor.

Structural Determinants for Protein adsorption/non-adsorption to Silica Surface

The understanding of the mechanisms involved in the interaction of proteins with inorganic surfaces is of major interest in both fundamental research and applications such as nanotechnology. However, despite intense research, the mechanisms and the structural determinants of protein/surface interactions are still unclear. We developed a strategy consisting in identifying, in a mixture of hundreds of soluble proteins, those proteins that are adsorbed on the surface and those that are not. If the two protein subsets are large enough, their statistical comparative analysis must reveal the physicochemical determinants relevant for adsorption versus non-adsorption. This methodology was tested with silica nanoparticles. We found that the adsorbed proteins contain a higher number of charged amino acids, particularly arginine, which is consistent with involvement of this basic amino acid in electrostatic interactions with silica. The analysis also identified a marked bias toward low aromatic amino acid content (phenylalanine, tryptophan, tyrosine and histidine) in adsorbed proteins. Structural analyses and molecular dynamics simulations of proteins from the two groups indicate that non-adsorbed proteins have twice as many π-π interactions and higher structural rigidity. The data are consistent with the notion that adsorption is correlated with the flexibility of the protein and with its ability to spread on the surface. Our findings led us to propose a refined model of protein adsorption.

Genome-wide location analysis reveals a role for Sub1 in RNA polymerase III transcription

Human PC4 and the yeast ortholog Sub1 have multiple functions in RNA polymerase II transcription. Genome-wide mapping revealed that Sub1 is present on Pol III-transcribed genes. Sub1 was found to interact with components of the Pol III transcription system and to stimulate the initiation and reinitiation steps in a system reconstituted with all recombinant factors. Sub1 was required for optimal Pol III gene transcription in exponentially growing cells.

InteroPORC: an automated tool to predict highly conserved protein interaction networks

Motivation Protein-protein interaction networks provide insights into the relationships between the proteins of an organism thereby contributing to a better understanding of cellular processes. Nevertheless, large-scale interaction networks are available for only a few model organisms but lack for most species. Thus, the interolog concept is useful to transfer interactions onto a target species. The idea is to combine known interactions from a source species with orthology relationships between source and target species (see Figure 1). Such transfers have already been done for a limited number of species. However, no software or standard method was available for that purpose so far. That is the reason why we decided to develop such a prediction tool.

Mining Biological Data Using Pyramids

This paper is a review of promising applications of pyramidal classification to biological data. We show that overlapping and ordering properties can give new insights that can not be achieved using more classical methods. We examplify our point using three applications: (i) a genome scale sequence analysis, (ii) a new progressive multiple sequence alignment method, (iii) a cluster analysis of transcriptomic data.