Nutrigenomics approach elucidates health-promoting effects of high vegetable intake in lean and obese men

Abstract We aimed to explore whether vegetable consumption according to guidelines has beneficial health effects determined with classical biomarkers and nutrigenomics technologies. Fifteen lean (age 36 ± 7 years; BMI 23.4 ± 1.7 kg m -2 ) and 17 obese (age 40 ± 6 years; BMI 30.3 ± 2.4 kg m -2 ) men consumed 50-or 200-g vegetables for 4 weeks in a randomized, crossover trial. Afterward, all subjects underwent 4 weeks of energy restriction (60 % of normal energy intake). Despite the limited weight loss of 1.7 ± 2.4 kg for the lean and 2.1 ± 1.9 kg for the obese due to energy restriction, beneficial health effects were found, including lower total cholesterol, LDL cholesterol and HbA1c concentrations. The high vegetable intake resulted in increased levels of plasma amino acid metabolites, decreased levels of 9-HODE and prostaglandin D3 and decreased levels of ASAT and ALP compared to low vegetable intake. Adipose tissue gene expression changes in response to vegetable intake were identified, and sets of selected genes were submitted to network analysis. The network of inflammation genes illustrated a central role for NFkB in (adipose tissue) modulation of inflammation by increased vegetable intake, in lean as well as obese subjects. In obese subjects, high vegetable intake also resulted in changes related to energy metabolism, adhesion and inflammation. By inclusion of sensitive omics technologies and comparing the changes induced by high vegetable intake with changes induced by energy restriction, it has been shown that part of vegetables' health benefits are mediated by changes in energy metabolism, inflammatory processes and oxidative stress

Insight in modulation of inflammation in response to diclofenac intervention: a human intervention study

Background. Chronic systemic low-grade inflammation in obese subjects is associated with health complications including cardiovascular diseases, insulin resistance and diabetes. Reducing inflammatory responses may reduce these risks. However, available markers of inflammatory status inadequately describe the complexity of metabolic responses to mild anti-inflammatory therapy. Methods. To address this limitation, we used an integrative omics approach to characterize modulation of inflammation in overweight men during an intervention with the non-steroidal anti-inflammatory drug diclofenac. Measured parameters included 80 plasma proteins, >300 plasma metabolites (lipids, free fatty acids, oxylipids and polar compounds) and an array of peripheral blood mononuclear cells (PBMC) gene expression products. These measures were submitted to multivariate and correlation analysis and were used for construction of biological response networks. Results. A panel of genes, proteins and metabolites, including PGE2 and TNF-alpha, were identified that describe a diclofenac-response network (68 genes in PBMC, 1 plasma protein and 4 plasma metabolites). Novel candidate markers of inflammatory modulation included PBMC expression of annexin A1 and caspase 8, and the arachidonic acid metabolite 5,6-DHET. Conclusion. In this study the integrated analysis of a wide range of parameters allowed the development of a network of markers responding to inflammatory modulation, thereby providing insight into the complex process of inflammation and ways to assess changes in inflammatory status associated with obesity. Trial registration. The study is registered as NCT00221052 in clinicaltrials.gov database. © 2010 van Erk et al; licensee BioMed Central Ltd

Between Metabolite Relationships: an essential aspect of metabolic change

Not only the levels of individual metabolites, but also the relations between the levels of different metabolites may indicate (experimentally induced) changes in a biological system. Component analysis methods in current ‘standard’ use for metabolomics, such as Principal Component Analysis (PCA), do not focus on changes in these relations. We therefore propose the concept of ‘Between Metabolite Relationships’ (BMRs): common changes in the covariance (or correlation) between all metabolites in an organism. Such structural changes may indicate metabolic change brought about by experimental manipulation but which are lost with standard data analysis methods. These BMRs can be analysed by the INdividual Differences SCALing (INDSCAL) method. First the BMR quantification is described and subsequently the INDSCAL method. Finally, two studies illustrate the power and the applicability of BMRs in metabolomics. The first study is about the induced plant response of cabbage to herbivory, of which BMRs are a considerable part. In the second study—a human nutritional intervention study of green tea extract—standard data analysis tools did not reveal any metabolic change, although the BMRs were considerably affected. The presented results show that BMRs can be easily implemented in a wide variety of metabolomic studies. They provide a new source of information to describe biological systems in a way that fits flawlessly into the next generation of systems biology questions, dealing with personalized responses

Open data set of live cyanobacterial cells imaged using an X-ray laser

Structural studies on living cells by conventional methods are limited to low resolution because radiation damage kills cells long before the necessary dose for high resolution can be delivered. X-ray free-electron lasers circumvent this problem by outrunning key damage processes with an ultra-short and extremely bright coherent X-ray pulse. Diffraction-before-destruction experiments provide high-resolution data from cells that are alive when the femtosecond X-ray pulse traverses the sample. This paper presents two data sets from micron-sized cyanobacteria obtained at the Linac Coherent Light Source, containing a total of 199,000 diffraction patterns. Utilizing this type of diffraction data will require the development of new analysis methods and algorithms for studying structure and structural variability in large populations of cells and to create abstract models. Such studies will allow us to understand living cells and populations of cells in new ways. New X-ray lasers, like the European XFEL, will produce billions of pulses per day, and could open new areas in structural sciences

A new benchmark dataset with production methodology for short text semantic similarity algorithms

This research presents a new benchmark dataset for evaluating Short Text Semantic Similarity (STSS) measurement algorithms and the methodology used for its creation. The power of the dataset is evaluated by using it to compare two established algorithms, STASIS and Latent Semantic Analysis. This dataset focuses on measures for use in Conversational Agents; other potential applications include email processing and data mining of social networks. Such applications involve integrating the STSS algorithm in a complex system, but STSS algorithms must be evaluated in their own right and compared with others for their effectiveness before systems integration. Semantic similarity is an artifact of human perception; therefore its evaluation is inherently empirical and requires benchmark datasets derived from human similarity ratings. The new dataset of 64 sentence pairs, STSS-131, has been designed to meet these requirements drawing on a range of resources from traditional grammar to cognitive neuroscience. The human ratings are obtained from a set of trials using new and improved experimental methods, with validated measures and statistics. The results illustrate the increased challenge and the potential longevity of the STSS-131 dataset as the Gold Standard for future STSS algorithm evaluation. © 2013 ACM 1550-4875/2013/12-ART17 15.00

Metal Bioavailability in the Sava River Water

Metals present one of the major contamination problems for freshwater systems, such as the Sava River, due to their high toxicity, persistence, and tendency to accumulate in sediment and living organisms. The comprehensive assessment of the metal bioavailability in the Sava River encompassed the analyses of dissolved and DGT-labile metal species of nine metals (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) in the river water, as well as the evaluation of the accumulation of five metals (Cd, Cu, Fe, Mn, and Zn) in three organs (liver, gills, and gastrointestinal tissue) of the bioindicator organism, fish species European chub (Squalius cephalus L.).This survey was conducted mainly during the year 2006, in two sampling campaigns, in April/May and September, as periods representative for chub spawning and post-spawning. Additionally, metal concentrations were determined in the intestinal parasites acanthocephalans, which are known for their high affinity for metal accumulation. Metallothionein concentrations were also determined in three chub organs, as a commonly applied biomarker of metal exposure. Based on the metal concentrations in the river water, the Sava River was defined as weakly contaminated and mainly comparable with unpolluted rivers, which enabled the analyses of physiological variability of metal and metallothionein concentrations in the chub organs, as well as the establishment of their constitutive levels