The Gram-negative bacterial lipopolysaccharide (LPS) is a major component of the outer membrane that plays a key role in host–pathogen interactions with the innate immune system. During infection, bacteria are exposed to a host environment that is typically dominated by inflammatory cells and soluble factors, including antibiotics, which provide cues about regulation of gene expression. Bacterial adaptive changes including modulation of LPS synthesis and structure are a conserved theme in infections, irrespective of the type or bacteria or the site of infection. In general, these changes result in immune system evasion, persisting inflammation and increased antimicrobial resistance. Here, we review the modifications of LPS structure and biosynthetic pathways that occur upon adaptation of model opportunistic pathogens (Pseudomonas aeruginosa, Burkholderia cepacia complex bacteria, Helicobacter pylori and Salmonella enterica) to chronic infection in respiratory and gastrointestinal sites. We also discuss the molecular mechanisms of these variations and their role in the host–pathogen interaction.

We present the YEAst Search for Transcriptional Regulators And Consensus Tracking (YEASTRACT; www.yeastract.com) database, a tool for the analysis of transcription regulatory associations in Saccharomyces cerevisiae. This database is a repository of 12 346 regulatory associations between transcription factors and target genes, based on experimental evidence which was spread throughout 861 bibliographic references. It also includes 257 specific DNA-binding sites for more than a hundred characterized transcription factors. Further information about each yeast gene included in the database was obtained from Saccharomyces Genome Database (SGD), Regulatory Sequences Analysis Tools and Gene Ontology (GO) Consortium. Computational tools are also provided to facilitate the exploitation of the gathered data when solving a number of biological questions as exemplified in the Tutorial also available on the system. YEASTRACT allows the identification of documented or potential transcription regulators of a given gene and of documented or potential regulons for each transcription factor. It also renders possible the comparison between DNA motifs, such as those found to be over-represented in the promoter regions of co-regulated genes, and the transcription factor-binding sites described in the literature. The system also provides an useful mechanism for grouping a list of genes (for instance a set of genes with similar expression profiles as revealed by microarray analysis) based on their regulatory associations with known transcription factors.

Weak acids are widely used as food preservatives (e.g., acetic, propionic, benzoic, and sorbic acids), herbicides (e.g., 2,4-dichlorophenoxyacetic acid), and as antimalarial (e.g., artesunic and artemisinic acids), anticancer (e.g., artesunic acid), and immunosuppressive (e.g., mycophenolic acid) drugs, among other possible applications. The understanding of the mechanisms underlying the adaptive response and resistance to these weak acids is a prerequisite to develop more effective strategies to control spoilage yeasts, and the emergence of resistant weeds, drug resistant parasites or cancer cells. Furthermore, the identification of toxicity mechanisms and resistance determinants to weak acid-based pharmaceuticals increases current knowledge on their cytotoxic effects and may lead to the identification of new drug targets. This review integrates current knowledge on the mechanisms of toxicity and tolerance to weak acid stress obtained in the model eukaryote Saccharomyces cerevisiae using genome-wide approaches and more detailed gene-by-gene analysis. The major features of the yeast response to weak acids in general, and the more specific responses and resistance mechanisms towards a specific weak acid or a group of weak acids, depending on the chemical nature of the side chain R group (R-COOH), are highlighted. The involvement of several transcriptional regulatory networks in the genomic response to different weak acids is discussed, focusing on the regulatory pathways controlled by the transcription factors Msn2p/Msn4p, War1p, Haa1p, Rim101p, and Pdr1p/Pdr3p, which are known to orchestrate weak acid stress response in yeast. The extrapolation of the knowledge gathered in yeast to other eukaryotes is also attempted.

• The activation of high-affinity root transport systems is the best-conserved strategy employed by plants to cope with low inorganic phosphate (Pi) availability, a role traditionally assigned to Pi transporters of the Pht1 family, whose respective contributions to Pi acquisition remain unclear. • To characterize the Arabidopsis thaliana Pht1;9 transporter, we combined heterologous functional expression in yeast with expression/subcellular localization studies and reverse genetics approaches in planta. Double Pht1;9/Pht1;8 silencing lines were also generated to gain insight into the role of the closest Pht1;9 homolog. • Pht1;9 encodes a functional plasma membrane-localized transporter that mediates high-affinity Pi/H⁺ symport activity in yeast and is highly induced in Pi-starved Arabidopsis roots. Null pht1;9 alleles exhibit exacerbated responses to prolonged Pi limitation and enhanced tolerance to arsenate exposure, whereas Pht1;9 overexpression induces the opposite phenotypes. Strikingly, Pht1;9/Pht1;8 silencing lines display more pronounced defects than the pht1;9 mutants. • Pi and arsenic plant content analyses confirmed a role of Pht1;9 in Pi acquisition during Pi starvation and arsenate uptake at the root–soil interface. Although not affecting plant internal Pi repartition, Pht1;9 activity influences the overall Arabidopsis Pi status. Finally, our results indicate that both the Pht1;9 and Pht1;8 transporters function in sustaining plant Pi supply on environmental Pi depletion.

The YEASTRACT (http://www.yeastract.com) information system is a tool for the analysis and prediction of transcription regulatory associations in Saccharomyces cerevisiae. Last updated in June 2013, this database contains over 200 000 regulatory associations between transcription factors (TFs) and target genes, including 326 DNA binding sites for 113 TFs. All regulatory associations stored in YEASTRACT were revisited and new information was added on the experimental conditions in which those associations take place and on whether the TF is acting on its target genes as activator or repressor. Based on this information, new queries were developed allowing the selection of specific environmental conditions, experimental evidence or positive/negative regulatory effect. This release further offers tools to rank the TFs controlling a gene or genome-wide response by their relative importance, based on (i) the percentage of target genes in the data set; (ii) the enrichment of the TF regulon in the data set when compared with the genome; or (iii) the score computed using the TFRank system, which selects and prioritizes the relevant TFs by walking through the yeast regulatory network. We expect that with the new data and services made available, the system will continue to be instrumental for yeast biologists and systems biology researchers.