Genetic variation in thioredoxin interacting protein (TXNIP) is associated with hypertriglyceridaemia and blood pressure in diabetes mellitus

Aims Thioredoxin interacting protein (TXNIP) is an attractive candidate gene for diabetes or diabetic dyslipidaemia, since TXNIP is the strongest glucose-responsive gene in pancreatic B-cells, TXNIP deficiency in a mouse model is associated with hyperlipidaemia and TXNIP is located in the 1q21-1q23 chromosomal Type 2 diabetes mellitus (DM) locus. We set out to investigate whether metabolic effects of TXNIP that were previously reported in a murine model are also relevant in human Type 2 DM. Methods The frequency distribution of a 3' UTR single nucleotide polymorphism (SNP) in TXNIP was investigated in subjects with normal glucose tolerance (NGT; n = 379), impaired glucose tolerance (IGT; n = 228) and Type 2 DM (n = 230). Metabolic data were used to determine the effect of this SNP on parameters associated with lipid and glucose metabolism. Results The frequency of the TXNIP variation did not differ between groups, but within the group of diabetic subjects, carriers of the TXNIP-T variant had 1.6-fold higher triglyceride concentrations (P = 0.015; n = 136) and a 5.5-mmHg higher diastolic blood pressure (P = 0.02; n = 212) than homozygous carriers of the common C-allele, whereas in non-diabetic subjects fasting glucose was 0.26 mmol/l lower (P = 0.002; n = 478) in carriers of the T-allele. Moreover, a significant interaction between plasma glucose concentrations and TXNIP polymorphism on plasma triglycerides was observed (P = 0.012; n = 544). Conclusion This is the first report to implicate TXNIP in a human disorder of energy metabolism, Type 2 diabetes. The effect of TXNIP on triglycerides is influenced by plasma glucose concentrations, suggesting that the biological relevance of TXNIP variations may be particularly relevant in recurrent episodes of hyperglycaemia

The FAIR Guiding Principles for scientific data management and stewardship

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community

Tehnološki aspekti industrijske proizvodnje dječje hrane na bazi mlijeka

Idealna hrana za dojenčad - ženino mlijeko, nije uvijek praktički na raspolaganju ili ga nema u dovoljnoj količini. Kao najbolja i jedina zamjena tome mlijeku, odnosno kao sirovina za preradu u dojenačku i dječju hranu, služi kravlje mlijeko

A network biology workflow to study transcriptomics data of the diabetic liver

BACKGROUND: Nowadays a broad collection of transcriptomics data is publicly available in online repositories. Methods for analyzing these data often aim at deciphering the influence of gene expression at the process level. Biological pathway diagrams depict known processes and capture the interactions of gene products and metabolites, information that is essential for the computational analysis and interpretation of transcriptomics data.The present study describes a comprehensive network biology workflow that integrates differential gene expression in the human diabetic liver with pathway information by building a network of interconnected pathways. Worldwide, the incidence of type 2 diabetes mellitus is increasing dramatically, and to better understand this multifactorial disease, more insight into the concerted action of the disease-related processes is needed. The liver is a key player in metabolic diseases and diabetic patients often develop non-alcoholic fatty liver disease. RESULTS: A publicly available dataset comparing the liver transcriptome from lean and healthy vs. obese and insulin-resistant subjects was selected after a thorough analysis. Pathway analysis revealed seven significantly altered pathways in the WikiPathways human pathway collection. These pathways were then merged into one combined network with 408 gene products, 38 metabolites and 5 pathway nodes. Further analysis highlighted 17 nodes present in multiple pathways, and revealed the connections between different pathways in the network. The integration of transcription factor-gene interactions from the ENCODE project identified new links between the pathways on a regulatory level. The extension of the network with known drug-target interactions from DrugBank allows for a more complete study of drug actions and helps with the identification of other drugs that target proteins up- or downstream which might interfere with the action or efficiency of a drug. CONCLUSIONS: The described network biology workflow uses state-of-the-art pathway and network analysis methods to study the rewiring of the diabetic liver. The integration of experimental data and knowledge on disease-affected biological pathways, including regulatory elements like transcription factors or drugs, leads to improved insights and a clearer illustration of the overall process. It also provides a resource for building new hypotheses for further follow-up studies. The approach is highly generic and can be applied in different research fields

The role of bioinformatics in pathway curation

Diagrams and models of biological pathways are useful tools in biology. Pathway diagrams are mainly used for illustrative purposes for instance in textbooks and in presentations. Pathway models are used in the analysis of genomic data. Bridging the gap between diagrams and models allows not only the analysis of genomics data and interactions but also the visualisation of the results in a variety of different ways. The knowledge needed for pathway creation and curation is available from three distinct sources: databases, literature and experts. We describe the role of bioinformatics in facilitating the creation and curation of pathway

Validating nutrient-related gene expression changes from microarrays using RT(2) PCR-arrays

Microarray technology allows us to perform high-throughput screening of changes in gene expression. The outcome of microarray experiments largely depends on the applied analysis methods and cut-off values chosen. Results are often required to be verified using a more sensitive detection technique, such as quantitative real-time PCR (qPCR or RT-PCR). Throughout the years, this technique has become a de facto golden standard. Individual qPCRs are time-consuming, but the technology to perform high-throughput qPCR reactions has become available through PCR-arrays that allow up to 384 PCR reactions simultaneously. Our current aim was to investigate the usability of a RT(2) Profiler™ PCR-array as validation in a nutritional intervention study, where the measured changes in gene expression were low. For some differentially expressed genes, the PCR-array confirmed the microarray prediction, though not for all. Furthermore, the PCR-array allowed picking up the expression of genes that were not measurable on the microarray platform but also vice versa. We conclude that both techniques have their own (dis)advantages and specificities, and for less pronounced changes using both technologies may be useful as complementation rather than validation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12263-008-0094-1) contains supplementary material, which is available to authorized users

A systematic review of large scale and heterogeneous gene array data in heart failure

Microarray analysis has become a widely available tool for the generation of gene expression data on a genomic scale. Since the studies with similar protocols are growing, it has become necessary to systematically revise the large body of literature to decipher the gene expression data. In this review, we analyzed and critically discussed the database presented from 14 published studies that showed the gene expression profile in heart failure (HF) using microarray as a primary tool. After comparing the diverse database from these studies, we explain the protein translational, matri-cellular, immunological and fibrosis-related mechanisms in HF. In addition to previously annotated genes, we analyzed two differentially expressed expressed sequence tags (ESTs) (KIAA0152 and Suppressor of G(Two) allele of the suppressor of kinetochore protein-1, SGT1) in HF and showed how bio-informatic analysis of ESTs can lead to the identification of novel pathways active in HF. We have also discussed the new publicly accessible tools that link the gene expression data to gene ontogeny (GO) and functionality. Finally, we have systematically revised the chromosomal localization of the genes that are specifically up-regulated in HF. We have thus spotted chromosome 1, 2, 11 and 12 as the chromosomal hotspots of HF. This methodical approach will simplify the existing concepts on the evolution and progression of HF and lead us toward the development of newer diagnostic and therapeutic tools. Although modeled to HF, this approach should be of broader scientific interest to elaborate multiple genes and complex pathways

Investigating the Molecular Processes behind the Cell-Specific Toxicity Response to Titanium Dioxide Nanobelts

Some engineered nanomaterials incite toxicological effects, but the underlying molecular processes are understudied. The varied physicochemical properties cause different initial molecular interactions, complicating toxicological predictions. Gene expression data allow us to study the responses of genes and biological processes. Overrepresentation analysis identifies enriched biological processes using the experimental data but prompts broad results instead of detailed toxicological processes. We demonstrate a targeted filtering approach to compare public gene expression data for low and high exposure on three cell lines to titanium dioxide nanobelts. Our workflow finds cell and concentration-specific changes in affected pathways linked to four Gene Ontology terms (apoptosis, inflammation, DNA damage, and oxidative stress) to select pathways with a clear toxicity focus. We saw more differentially expressed genes at higher exposure, but our analysis identifies clear differences between the cell lines in affected processes. Colorectal adenocarcinoma cells showed resilience to both concentrations. Small airway epithelial cells displayed a cytotoxic response to the high concentration, but not as strongly as monocytic-like cells. The pathway-gene networks highlighted the gene overlap between altered toxicity-related pathways. The automated workflow is flexible and can focus on other biological processes by selecting other GO terms

WikiPathways App for Cytoscape: Making biological pathways amenable to network analysis and visualization

In this paper we present the open-source WikiPathways app for Cytoscape http://apps.cytoscape.org/apps/wikipathways) that can be used to import biological pathways for data visualization and network analysis. an open, collaborative biological pathway database that provides fully pathway diagrams for manual download or through web services. The app allows users to load pathways in two different views: as an ideal for data visualization and as a simple network to perform analysis. An example pathway and dataset are used to demonstrate the functionality of the WikiPathways app and how they can be combined and together with other apps. More than 3000 downloads in the first 12 following its release in August 2013 highlight the importance and app in the network biology field