Altered DNA methylation in liver and adipose tissues derived from individuals with obesity and type 2 diabetes.

BACKGROUND: Obesity is a well-recognized risk factor for insulin resistance and type 2 diabetes (T2D), although the precise mechanisms underlying the relationship remain unknown. In this study we identified alterations of DNA methylation influencing T2D pathogenesis, in subcutaneous and visceral adipose tissues, liver, and blood from individuals with obesity. METHODS: The study included individuals with obesity, with and without T2D. From these patients, we obtained samples of liver tissue (n = 16), visceral and subcutaneous adipose tissues (n = 30), and peripheral blood (n = 38). We analyzed DNA methylation using Illumina Infinium Human Methylation arrays, and gene expression profiles using HumanHT-12 Expression BeadChip Arrays. RESULTS: Analysis of DNA methylation profiles revealed several loci with differential methylation between individuals with and without T2D, in all tissues. Aberrant DNA methylation was mainly found in the liver and visceral adipose tissue. Gene ontology analysis of genes with altered DNA methylation revealed enriched terms related to glucose metabolism, lipid metabolism, cell cycle regulation, and response to wounding. An inverse correlation between altered methylation and gene expression in the four tissues was found in a subset of genes, which were related to insulin resistance, adipogenesis, fat storage, and inflammation. CONCLUSIONS: Our present findings provide additional evidence that aberrant DNA methylation may be a relevant mechanism involved in T2D pathogenesis among individuals with obesity

Disease Severity in Patients Infected with Leishmania mexicana Relates to IL-1β

Leishmania mexicana can cause both localized (LCL) and diffuse (DCL) cutaneous leishmaniasis, yet little is known about factors regulating disease severity in these patients. We analyzed if the disease was associated with single nucleotide polymorphisms (SNPs) in IL-1β (−511), CXCL8 (−251) and/or the inhibitor IL-1RA (+2018) in 58 Mexican mestizo patients with LCL, 6 with DCL and 123 control cases. Additionally, we analyzed the in vitro production of IL-1β by monocytes, the expression of this cytokine in sera of these patients, as well as the tissue distribution of IL-1β and the number of parasites in lesions of LCL and DCL patients. Our results show a significant difference in the distribution of IL-1β (−511 C/T) genotypes between patients and controls (heterozygous OR), with respect to the reference group CC, which was estimated with a value of 3.23, 95% CI = (1.2, 8.7) and p-value = 0.0167), indicating that IL-1β (−511 C/T) represents a variable influencing the risk to develop the disease in patients infected with Leishmania mexicana. Additionally, an increased in vitro production of IL-1β by monocytes and an increased serum expression of the cytokine correlated with the severity of the disease, since it was significantly higher in DCL patients heavily infected with Leishmania mexicana. The distribution of IL-1β in lesions also varied according to the number of parasites harbored in the tissues: in heavily infected LCL patients and in all DCL patients, the cytokine was scattered diffusely throughout the lesion. In contrast, in LCL patients with lower numbers of parasites in the lesions, IL-1β was confined to the cells. These data suggest that IL-1β possibly is a key player determining the severity of the disease in DCL patients. The analysis of polymorphisms in CXCL8 and IL-1RA showed no differences between patients with different disease severities or between patients and controls

A feature selection strategy for gene expression time series experiments with hidden Markov models.

Studies conducted in time series could be far more informative than those that only capture a specific moment in time. However, when it comes to transcriptomic data, time points are sparse creating the need for a constant search for methods capable of extracting information out of experiments of this kind. We propose a feature selection algorithm embedded in a hidden Markov model applied to gene expression time course data on either single or even multiple biological conditions. For the latter, in a simple case-control study features or genes are selected under the assumption of no change over time for the control samples, while the case group must have at least one change. The proposed model reduces the feature space according to a two-state hidden Markov model. The two states define change/no-change in gene expression. Features are ranked in consonance with three scores: number of changes across time, magnitude of such changes and quality of replicates as a measure of how much they deviate from the mean. An important highlight is that this strategy overcomes the few samples limitation, common in transcriptome experiments through a process of data transformation and rearrangement. To prove this method, our strategy was applied to three publicly available data sets. Results show that feature domain is reduced by up to 90% leaving only few but relevant features yet with findings consistent to those previously reported. Moreover, our strategy proved to be robust, stable and working on studies where sample size is an issue otherwise. Hence, even with two biological replicates and/or three time points our method proves to work well

Role of MicroRNAs in Signaling Pathways Associated with the Pathogenesis of Idiopathic Pulmonary Fibrosis: A Focus on Epithelial-Mesenchymal Transition

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive disease with high mortality and unclear etiology. Previous evidence supports that the origin of this disease is associated with epigenetic alterations, age, and environmental factors. IPF initiates with chronic epithelial lung injuries, followed by basal membrane destruction, which promotes the activation of myofibroblasts and excessive synthesis of extracellular matrix (ECM) proteins, as well as epithelial-mesenchymal transition (EMT). Due to miRNAs’ role as regulators of apoptosis, proliferation, differentiation, and cell-cell interaction processes, some studies have involved miRNAs in the biogenesis and progression of IPF. In this context, the analysis and discussion of the probable association of miRNAs with the signaling pathways involved in the development of IPF would improve our knowledge of the associated molecular mechanisms, thereby facilitating its evaluation as a therapeutic target for this severe lung disease. In this work, the most recent publications evaluating the role of miRNAs as regulators or activators of signal pathways associated with the pathogenesis of IPF were analyzed. The search in Pubmed was made using the following terms: “miRNAs and idiopathic pulmonary fibrosis (IPF)”; “miRNAs and IPF and signaling pathways (SP)”; and “miRNAs and IPF and SP and IPF pathogenesis”. Additionally, we focus mainly on those works where the signaling pathways involved with EMT, fibroblast differentiation, and synthesis of ECM components were assessed. Finally, the importance and significance of miRNAs as potential therapeutic or diagnostic tools for the treatment of IPF are discussed

A computational toxicogenomics approach identifies a list of highly hepatotoxic compounds from a large microarray database.

The liver and the kidney are the most common targets of chemical toxicity, due to their major metabolic and excretory functions. However, since the liver is directly involved in biotransformation, compounds in many currently and normally used drugs could affect it adversely. Most chemical compounds are already labeled according to FDA-approved labels using DILI-concern scale. Drug Induced Liver Injury (DILI) scale refers to an adverse drug reaction. Many compounds do not exhibit hepatotoxicity at early stages of development, so it is important to detect anomalies at gene expression level that could predict adverse reactions in later stages. In this study, a large collection of microarray data is used to investigate gene expression changes associated with hepatotoxicity. Using TG-GATEs a large-scale toxicogenomics database, we present a computational strategy to classify compounds by toxicity levels in human and animal models through patterns of gene expression. We combined machine learning algorithms with time series analysis to identify genes capable of classifying compounds by FDA-approved labeling as DILI-concern toxic. The goal is to define gene expression profiles capable of distinguishing the different subtypes of hepatotoxicity. The study illustrates that expression profiling can be used to classify compounds according to different hepatotoxic levels; to label those that are currently labeled as undertemined; and to determine if at the molecular level, animal models are a good proxy to predict hepatotoxicity in humans

Petržalka and the future of panel housing estates

Project points out possible methods of urbanistic and architectural work with panel housing estates, on specific example of Petržalka housing estate in Bratislava. Next to the possible alternatives of solution, which is this diploma project bringing on the table, its main importance is to move forward the thinking about panel housing estates, asking questions concerning our surrounding, and to discuss the future of environment we live in

Genetic Diversity of Tick-Borne Rickettsial Pathogens; Insights Gained from Distant Strains

The ability to capture genetic variation with unprecedented resolution improves our understanding of bacterial populations and their ability to cause disease. The goal of the pathogenomics era is to define genetic diversity that results in disease. Despite the economic losses caused by vector-borne bacteria in the Order Rickettsiales, little is known about the genetic variants responsible for observed phenotypes. The tick-transmitted rickettsial pathogen Anaplasma marginale infects cattle in tropical and subtropical regions worldwide, including Australia. Genomic analysis of North American A. marginale strains reveals a closed core genome defined by high levels of Single Nucleotide Polymorphisms (SNPs). Here we report the first genome sequences and comparative analysis for Australian strains that differ in virulence and transmissibility. A list of genetic differences that segregate with phenotype was evaluated for the ability to distinguish the attenuated strain from virulent field strains. Phylogenetic analyses of the Australian strains revealed a marked evolutionary distance from all previously sequenced strains. SNP analysis showed a strikingly reduced genetic diversity between these strains, with the smallest number of SNPs detected between any two A. marginale strains. The low diversity between these phenotypically distinct bacteria presents a unique opportunity to identify the genetic determinants of virulence and transmission