Minimization of biosynthetic costs in adaptive gene expression responses of yeast to environmental changes

Yeast successfully adapts to an environmental stress by altering physiology and fine-tuning metabolism. This fine-tuning is achieved through regulation of both gene expression and protein activity, and it is shaped by various physiological requirements. Such requirements impose a sustained evolutionary pressure that ultimately selects a specific gene expression profile, generating a suitable adaptive response to each environmental change. Although some of the requirements are stress specific, it is likely that others are common to various situations. We hypothesize that an evolutionary pressure for minimizing biosynthetic costs might have left signatures in the physicochemical properties of proteins whose gene expression is fine-tuned during adaptive responses. To test this hypothesis we analyze existing yeast transcriptomic data for such responses and investigate how several properties of proteins correlate to changes in gene expression. Our results reveal signatures that are consistent with a selective pressure for economy in protein synthesis during adaptive response of yeast to various types of stress. These signatures differentiate two groups of adaptive responses with respect to how cells manage expenditure in protein biosynthesis. In one group, significant trends towards downregulation of large proteins and upregulation of small ones are observed. In the other group we find no such trends. These results are consistent with resource limitation being important in the evolution of the first group of stress responses.

Forage preservation (grazing vs. hay) fed to ewes affects the fatty acid profile of milk and CPT1B gene expression in the sheep mammary gland

Background: Alterations in lipid metabolism occur when animals are exposed to different feeding systems. In the last few decades, the characterisation of genes involved in fat metabolism and technological advances have enabled the study of the effect of diet on the milk fatty acid (FA) profile in the mammary gland and aided in the elucidation of the mechanisms of the response to diet. The aim of this study was to evaluate the effect of different forage diets (grazing vs. hay) near the time of ewe parturition on the relationship between the fatty acid profile and gene expression in the mammary gland of the Churra Tensina sheep breed. Results: In this study, the forage type affected the C18:2 cis-9 trans-11 (CLA) and long-chain saturated fatty acid (LCFA) content, with higher percentages during grazing than during hay feeding. This may suggest that these FAs act as regulatory factors for the transcriptional control of the carnitine palmitoyltransferase 1B (CPT1B) gene, which was more highly expressed in the grazing group (GRE). The most highly expressed gene in the mammary gland at the fifth week of lactation is CAAT/ enhancer- binding protein beta (CEBPB), possibly due to its role in milk fat synthesis in the mammary gland. More stable housekeeping genes in the ovine mammary gland that would be appropriate for use in gene expression studies were ribosomal protein L19 (RPL19) and glyceraldehyde- 3- phosphate dehydrogenase (GAPDH). Conclusions: Small changes in diet, such as the forage preservation (grazing vs. hay), can affect the milk fatty acid profile and the expression of the CPT1B gene, which is associated with the oxidation of fatty acids. When compared to hay fed indoors, grazing fresh low mountain pastures stimulates the milk content of CLA and LCFA via mammary uptake. In this sense, LCFA in milk may be acting as a regulatory factor for transcriptional control of the CPT1B gene, which was more highly expressed in the grazing group.

Gene Set Analysis for improving genetic association studies

Introduction. Genetic epidemiology is focused on the study of the genetic causes that determine health and diseases in populations. To achieve this goal a common strategy is to explore differences in genetic variability between diseased and nondiseased individuals. Usual markers of genetic variability are single nucleotide polymorphisms (SNPs) which are changes in just one base in the genome. The usual statistical approach in genetic epidemiology study is a marginal analysis, where each SNP is analyzed separately for association with the phenotype. Motivation. It has been observed, that for common diseases the single-SNP analysis is not very powerful for detecting genetic causing variants. In this work, we consider Gene Set Analysis (GSA) as an alternative to standard marginal association approaches. GSA aims to assess the overall association of a set of genetic variants with a phenotype and has the potential to detect subtle effects of variants in a gene or a pathway that might be missed when assessed individually. Objective. We present a new optimized implementation of a pair of gene set analysis methodologies for analyze the individual evidence of SNPs in biological pathways. We perform a simulation study for exploring the power of the proposed methodologies in a set of scenarios with different number of causal SNPs under different effect sizes. In addition, we compare the results with the usual single-SNP analysis method. Moreover, we show the advantage of using the proposed gene set approaches in the context of an Alzheimer disease case-control study where we explore the Reelin signal pathway.

Developmental and tissue-specific involvement of peroxisome proliferator-activated receptor-alpha in the control of mouse uncoupling protein-3 gene expression.

Uncoupling protein-3 (UCP3) is a member of the mitochondrial carrier family expressed preferentially in skeletal muscle and heart. It appears to be involved in metabolic handling of fatty acids in a way that minimizes excessive production of reactive oxygen species. Fatty acids are powerful regulators of UCP3 gene transcription. We have found that the role of peroxisome proliferator-activated receptor-α (PPARα) on the control of UCP3 gene expression depends on the tissue and developmental stage. In adults, UCP3 mRNA expression is unaltered in skeletal muscle from PPARα-null mice both in basal conditions and under the stimulus of starvation. In contrast, UCP3 mRNA is down-regulated in adult heart both in fed and fasted PPARα-null mice. This occurs despite the increased levels of free fatty acids caused by fasting in PPARα-null mice. In neonates, PPARα-null mice show impaired UCP3 mRNA expression in skeletal muscle in response to milk intake, and this is not a result of reduced free fatty acid levels. The murine UCP3 promoter is activated by fatty acids through either PPARα or PPARδ but not by PPARγ or retinoid X receptor alone. PPARδ-dependent activation could be a potential compensatory mechanism to ensure appropriate expression of UCP3 gene in adult skeletal muscle in the absence of PPARα. However, among transcripts from other PPARα and PPARδ target genes, only those acutely induced by milk intake in wild-type neonates were altered in muscle or heart from PPARα-null neonates. Thus, PPARα-dependent regulation is required for appropriate gene regulation of UCP3 as part of the subset of fatty-acid-responsive genes in neonatal muscle and heart.

Identification of molecular subtypes and gene expression patterns of breast cancer analysing RNA-seq data

Breast cancer is the most common diagnosed cancer and the leading cause of cancer death among females worldwide. It is considered a highly heterogeneous disease and it must be classified into more homogeneous groups. Hence, the purpose of this study was to classify breast tumors based on variations in gene expression patterns derived from RNA sequencing by using different class discovery methods. 42 breast tumors paired-samples were sequenced by Illumine Genome Analyzer and the data was analyzed and prepared by TopHat2 and htseq-count. As reported previously, breast cancer could be grouped into five main groups known as basal epithelial-like group, HER2 group, normal breast-like group and two Luminal groups with a distinctive expression profile. Classifying breast tumor samples by using PAM50 method, the most common subtype was Luminal B and was significantly associated with ESR1 and ERBB2 high expression. Luminal A subtype had ESR1 and SLC39A6 significant high expression, whereas HER2 subtype had a high expression of ERBB2 and CNNE1 genes and low luminal epithelial gene expression. Basal-like and normal-like subtypes were associated with low expression of ESR1, PgR and HER2, and had significant high expression of cytokeratins 5 and 17. Our results were similar compared with TGCA breast cancer data results and with known studies related with breast cancer classification. Classifying breast tumors could add significant prognostic and predictive information to standard parameters, and moreover, identify marker genes for each subtype to find a better therapy for patients with breast cancer.

Establishing Zebrafish as a Novel Exercise Model: Swimming Economy, Swimming-enhanced Growth and Muscle Growth Marker Gene Expression

Zebrafish has been largely accepted as a vertebrate multidisciplinary model but its usefulness as a model for exercise physiology has been hampered by the scarce knowledge on its swimming economy, optimal swimming speeds and cost of transport. Therefore, we have performed individual and group-wise swimming experiments to quantify swimming economy and to demonstrate the exercise effects on growth in adult zebrafish.