Yeast Saccharomyces cerevisiae adiponectin receptor homolog Izh2 is involved in the regulation of zinc, phospholipid and pH homeostasis

[EN] The functional link between zinc homeostasis and membrane-related processes, including lipid metabolism regulation, extends from yeast to humans, and has a likely role in the pathogenesis of diabetes. The yeast Izh2 protein has been previously implicated in zinc ion homeostasis and in the regulation of lipid and phosphate metabolism, but its precise molecular function is not known. We performed a chemogenomics experiment to determine the genes conferring resistance or sensitivity to different environmental zinc concentrations. We then determined at normal, depleted and excess zinc concentrations, the genetic interactions of IZH2 at the genome-wide level and measured changes in the transcriptome caused by deletion of IZH2. We found evidence for an important cellular function of the Rim101 pathway in zinc homeostasis in neutral or acidic environments, and observed that phosphatidylinositol is a source of inositol when zinc availability is limited. Comparison of our experimental profiles with published gene expression and genetic interaction profiles revealed pleiotropic functions for Izh2. We propose that Izh2 acts as an integrator of intra- and extracellular signals in providing adequate cellular responses to maintain homeostasis under different external conditions, including but not limited to alterations in zinc concentrations. Guardar / Salir Siguiente >This work was supported by grant P1-0207 from the Slovenian Research Agency. M.M.U. was supported by the Young Investigator fellowship scheme from the Slovenian Research Agency. Work done in the group of L.Y. was funded by grant BFU2011-30197-C03-03 from the Spanish Ministry of Science and Innovation (Madrid, Spain). C.P. was supported by a pre-doctoral fellowship from the Spanish Research Council.Mattiazzi Usaj, M.; Prelec, M.; Brioznic, M.; Primo Planta, C.; Curk, T.; Scancar, J.; Yenush, L.... (2015). Yeast Saccharomyces cerevisiae adiponectin receptor homolog Izh2 is involved in the regulation of zinc, phospholipid and pH homeostasis. Metallomics. 7(9):1338-1351. https://doi.org/10.1039/c5mt00095e133813517

Transcriptomics unravels the adaptive molecular mechanisms of Brettanomyces bruxellensis under SO2 stress in wine condition

CITATION: Valdetara, F. et al. 2020. Transcriptomics unravels the adaptive molecular mechanisms of Brettanomyces bruxellensis under SO2 stress in wine condition. Food Microbiology, 90. doi:10.1016/j.fm.2020.103483.The original publication is available at https://www.sciencedirect.com/journal/food-microbiologySulfur dioxide is generally used as an antimicrobial in wine to counteract the activity of spoilage yeasts, including Brettanomyces bruxellensis. However, this chemical does not exert the same effectiveness on different B. bruxellensis yeasts since some strains can proliferate in the final product leading to a negative sensory profile due to 4-ethylguaiacol and 4-ethylphenol. Thus, the capability of deciphering the general molecular mechanisms characterizing this yeast species’ response in presence of SO2 stress could be considered strategic for a better management of SO2 in winemaking. A RNA-Seq approach was used to investigate the gene expression of two strains of B. bruxellensis, AWRI 1499 and CBS 2499 having different genetic backgrounds, when exposed to a SO2 pulse. Results revealed that sulphites affected yeast culturability and metabolism, but not volatile phenol production suggesting that a phenotypical heterogeneity could be involved for the SO2 cell adaptation. The transcriptomics variation in response to SO2 stress confirmed the strain-related response in B. bruxellensis and the GO analysis of common differentially expressed genes showed that the detoxification process carried out by SSU1 gene can be considered as the principal specific adaptive response to counteract the SO2 presence. However, nonspecific mechanisms can be exploited by cells to assist the SO2 tolerance; namely, the metabolisms related to sugar alcohol (polyols) and oxidative stress, and structural compounds.https://www.sciencedirect.com/science/article/pii/S0740002020300721?via%3DihubPublishers versio

Rule-based clustering for gene promoter structure discovery

BACKGROUND: The genetic cellular response to internal and external changes is determined by the sequence and structure of gene-regulatory promoter regions. OBJECTIVES: Using data on gene-regulatory elements (i.e., either putative or known transcription factor binding sites) and data on gene expression profiles we can discover structural elements in promoter regions and infer the underlying programs of gene regulation. Such hypotheses obtained in silico can greatly assist us in experiment planning. The principal obstacle for such approaches is the combinatorial explosion in different combinations of promoter elements to be examined. METHODS: Stemming from several state-of-the-art machine learning approaches we here propose a heuristic, rule-based clustering method that uses gene expression similarity to guide the search for informative structures in promoters, thus exploring only the most promising parts of the vast and expressively rich rule-space. RESULTS: We present the utility of the method in the analysis of gene expression data on budding yeast S. cerevisiae where cells were induced to proliferate peroxisomes. CONCLUSIONS: We demonstrate that the proposed approach is able to infer informative relations uncovering relatively complex structures in gene promoter regions that regulate gene expression

Inference of the Molecular Mechanism of Action from Genetic Interaction and Gene Expression Data

Inference of new and useful hypotheses from heterogeneous sources of genome-scale experimental data requires new computational methods that can integrate different types of data. Gene expression and genetic interaction data are two most informative data types, each allowing the identification of genes at different levels of cellular regulatory network hierarchy. We present an integrative data analysis approach, which, rather than correlating the findings from the two data sets, uses each type of data independently to identify the components of molecular pathways and combines them into a single directed network. Our computational genomics approach is based on a set of inference rules traditionally used for reasoning on genetic experiments, which we have formalized and implemented in a software tool. The approach uses chemogenetic interaction and expression data to infer the type of relation between the chemical substance (perturber) and a transcription factor by using previous knowledge on the set of genes whose expression the transcription factor in question regulates. We have used the proposed approach to successfully infer the models for the action of the drug rapamycin and of a DNA damaging agent on their molecular targets and pathways in yeast cells. The developed method is available as a web-based tool at http://www.ailab.si/perturbagen

Microarray data mining with visual programming

Visual programming offers an intuitive means of combining known analysis and visualization methods into powerful applications. The system presented here enables users who are not programmers to manage microarray and genomic data flow and to customize their analyses by combining common data analysis tools to fit their needs

Application of geophysical and multispectral imagery data for predictive mapping of a complex geo‑tectonic unit: a case study of the East Vardar Ophiolite Zone, North‑Macedonia

The Random Forest (RF) and K nearest neighbors (KNN) machine learning (ML) algorithms were evaluated for their ability to predict ophiolite occurrences, in the East Vardar Zone (EVZ) of central North Macedonia. A predictive map of the investigated area was created using three data sources: geophysical data (digital elevation model, gravity and geomagnetic), multispectral optical satellite images (Landsat 7 ETM + and their derivatives), and geological data (distance to fault map and ophiolite outcrops map). The research included a comparison and discussion on the statistical and geological findings derived from different training dataset class ratios in relation to a testing dataset characterized by significant class imbalance. The results suggest that the precise selection of a suitable class balance for the training dataset is a critical factor in achieving accurate ophiolite prediction with RF and KNN algorithms. The analysis of feature importance revealed that the Bouguer gravity anomaly map, total intensity of the Earth’s magnetic field reduced to the pole map, distance to fault map, band ratio BR3 map obtained from multispectral satellite images, and digital elevation model are the most significant features for predicting ophiolites within the EVZ. KNN showed poorer results compared to RF in terms of both the evaluation metrics and visual analysis of prediction maps. The methods applied in this research can be applied for predictive mapping of complex geo-tectonic units covered by dense vegetation, and may indicate the presence of these units even if they were not previously mapped, particularly when geophysical data are used as features

Socioeconomic Inequalities in Mental Health of Adult Population: Serbian National Health Survey

Background: The global burden of mental disorders is rising. In Serbia, anxiety is the leading cause of disability-adjusted life years. Serbia has no mental health survey at the population level. The information on prevalence of mental disorders and related socioeconomic inequalities are valuable for mental care improvement. Aims: То explore the prevalence of mental health disorders and socioeconomic inequalities in mental health of adult Serbian population, and to explore whether age years and employment status interact with mental health in urban and rural settlements. Study Design: Cross-sectional study. Methods: This study is an additional analysis of Serbian Health Survey 2006 that was carried out with standardized household questionnaires at the representative sample of 7673 randomly selected households – 15563 adults. The response rate was 93%. A multivariate logistic regression modeling highlighted the predictors of the 5 item Mental Health Inventory (MHI-5), and of chronic anxiety or depression within eight independent variables (age, gender, type of settlement, marital status and self-perceived health, education, employment status and Wealth Index). The significance level in descriptive statistics, chi square analysis and bivariate and multivariate logistic regressions was set at p<0.05. Results: Chronic anxiety or depression was seen in 4.9% of the respondents, and poor MHI-5 in 47% of respondents. Low education (Odds Ratios 1.32; 95% confidence intervals=1.16-1.51), unemployment (1.36; 1.18-1.56), single status (1.34; 1.23-1.45), and Wealth Index middle class (1.20; 1.08-1.32) or poor (1.33; 1.21-1.47) were significantly related with poor MHI-5. Unemployed persons in urban settlements had higher odds for poormMHI-5 than unemployed in rural areas (0.73; 0.59-0.89). Single (1.50; 1.26-1.78), unemployed (1.39; 1.07-1.80) and inactive respondents (1.42; 1.10-1.83) had a higher odds of chronic anxiety or depression than married individuals, or those with partner, and employed persons. Those with perceived good health status had lower odds for poor MHI-5, chronic anxiety or depression than those whose general health was average and poor. Conclusion: Almost half of the population assessed their mental health as poor and 5% had diagnosed chronic anxiety or depression. Multi-sectoral socioeconomic and female-sensitive policies should be wisely tailored to reduce mental health inequalities contributed by differences in age, education, employment, marriage and the wealth status of the adult population

Dietary amino acid and vitamin complex protects honey bee from immunosuppression caused by <i>Nosema ceranae</i>

<div><p>Microsporidium <i>Nosema ceranae</i> is well known for exerting a negative impact on honey bee health, including down-regulation of immunoregulatory genes. Protein nutrition has been proven to have beneficial effects on bee immunity and other aspects of bee health. Bearing this in mind, the aim of our study was to evaluate the potential of a dietary amino acid and vitamin complex “BEEWELL AminoPlus” to protect honey bees from immunosuppression induced by <i>N</i>. <i>ceranae</i>. In a laboratory experiment bees were infected with <i>N</i>. <i>ceranae</i> and treated with supplement on first, third, sixth and ninth day after emergence. The expression of genes for immune-related peptides (abaecin, apidaecin, hymenoptaecin, defensin and vitellogenin) was compared between groups. The results revealed significantly lower (p<0.01 or p<0.001) numbers of <i>Nosema</i> spores in supplemented groups than in the control especially on day 12 post infection. With the exception of abacein, the expression levels of immune-related peptides were significantly suppressed (p<0.01 or p<0.001) in control group on the 12^th day post infection, compared to bees that received the supplement. It was supposed that <i>N</i>. <i>ceranae</i> had a negative impact on bee immunity and that the tested amino acid and vitamin complex modified the expression of immune-related genes in honey bees compromised by infection, suggesting immune-stimulation that reflects in the increase in resistance to diseases and reduced bee mortality. The supplement exerted best efficacy when applied simultaneously with <i>Nosema</i> infection, which can help us to assume the most suitable period for its application in the hive.</p></div