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van der Voort M.,Wageningen University | Kempenaar M.,The Netherlands Bioinformatics Center | van Driel M.,The Netherlands Bioinformatics Center | Raaijmakers J.M.,Netherlands Institute of Ecology | And 2 more authors.
Ecology Letters | Year: 2016

The rhizosphere microbiome offers a range of ecosystem services to the plant, including nutrient acquisition and tolerance to (a)biotic stress. Here, analysing the data by Mendes et al. (2011), we show that short heat disturbances (50 or 80 °C, 1 h) of a soil suppressive to the root pathogenic fungus Rhizoctonia solani caused significant increase in alpha diversity of the rhizobacterial community and led to partial or complete loss of disease protection. A reassembly model is proposed where bacterial families that are heat tolerant and have high growth rates significantly increase in relative abundance after heat disturbance, while temperature-sensitive and slow-growing bacteria have a disadvantage. The results also pointed to a potential role of slow-growing, heat-tolerant bacterial families from Actinobacteria and Acidobacteria phyla in plant disease protection. In conclusion, short heat disturbance of soil results in rearrangement of rhizobacterial communities and this is correlated with changes in the ecosystem service disease suppression. © 2016 John Wiley & Sons Ltd/CNRS. Source

Van Schalkwijk D.B.,TNO | Van Schalkwijk D.B.,Leiden Amsterdam Center for Drug Research | Van Schalkwijk D.B.,The Netherlands Bioinformatics Center | van Ommen B.,TNO | And 4 more authors.
Journal of Clinical Bioinformatics | Year: 2011

Background: Dyslipidemia is an important risk factor for cardiovascular disease and type II diabetes. Lipoprotein diagnostics, such as LDL cholesterol and HDL cholesterol, help to diagnose these diseases. Lipoprotein profile measurements could improve lipoprotein diagnostics, but interpretational complexity has limited their clinical application to date. We have previously developed a computational model called Particle Profiler to interpret lipoprotein profiles. In the current study we further developed and calibrated Particle Profiler using subjects with specific genetic conditions. We subsequently performed technical validation and worked at an initial indication of clinical usefulness starting from available data on lipoprotein concentrations and metabolic fluxes. Since the model outcomes cannot be measured directly, the only available technical validation was corroboration. For an initial indication of clinical usefulness, pooled lipoprotein metabolic flux data was available from subjects with various types of dyslipidemia. Therefore we investigated how well lipoprotein metabolic ratios derived from Particle Profiler distinguished reported dyslipidemic from normolipidemic subjects.Results: We found that the model could fit a range of normolipidemic and dyslipidemic subjects from fifteen out of sixteen studies equally well, with an average 8.8% ± 5.0% fit error only one study showed a larger fit error. As initial indication of clinical usefulness, we showed that one diagnostic marker based on VLDL metabolic ratios better distinguished dyslipidemic from normolipidemic subjects than triglycerides, HDL cholesterol, or LDL cholesterol. The VLDL metabolic ratios outperformed each of the classical diagnostics separately; they also added power of distinction when included in a multivariate logistic regression model on top of the classical diagnostics.Conclusions: In this study we further developed, calibrated, and corroborated the Particle Profiler computational model using pooled lipoprotein metabolic flux data. From pooled lipoprotein metabolic flux data on dyslipidemic patients, we derived VLDL metabolic ratios that better distinguished normolipidemic from dyslipidemic subjects than standard diagnostics, including HDL cholesterol, triglycerides and LDL cholesterol. Since dyslipidemias are closely linked to cardiovascular disease and diabetes type II development, lipoprotein metabolic ratios are candidate risk markers for these diseases. These ratios can in principle be obtained by applying Particle Profiler to a single lipoprotein profile measurement, which makes clinical application feasible. © 2011 van Schalkwijk et al; licensee BioMed Central Ltd. Source

Van Schalkwijk D.B.,TNO | Van Schalkwijk D.B.,Leiden Amsterdam Center for Drug Research | Van Schalkwijk D.B.,The Netherlands Bioinformatics Center | Pasman W.J.,TNO | And 8 more authors.
PLoS ONE | Year: 2014

Dietary medium chain fatty acids (MCFA) and linoleic acid follow different metabolic routes, and linoleic acid activates PPAR receptors. Both these mechanisms may modify lipoprotein and fatty acid metabolism after dietary intervention. Our objective was to investigate how dietary MCFA and linoleic acid supplementation and body fat distribution affect the fasting lipoprotein subclass profile, lipoprotein kinetics, and postprandial fatty acid kinetics. In a randomized double blind cross-over trial, 12 male subjects (age 51±7 years; BMI 28.5±0.8 kg/m2), were divided into 2 groups according to waist-hip ratio. They were supplemented with 60 grams/day MCFA (mainly C8:0, C10:0) or linoleic acid for three weeks, with a washout period of six weeks in between. Lipoprotein subclasses were measured using HPLC. Lipoprotein and fatty acid metabolism were studied using a combination of several stable isotope tracers. Lipoprotein and tracer data were analyzed using computational modeling. Lipoprotein subclass concentrations in the VLDL and LDL range were significantly higher after MCFA than after linoleic acid intervention. In addition, LDL subclass concentrations were higher in lower body obese individuals. Differences in VLDL metabolism were found to occur in lipoprotein lipolysis and uptake, not production; MCFAs were elongated intensively, in contrast to linoleic acid. Dietary MCFA supplementation led to a less favorable lipoprotein profile than linoleic acid supplementation. These differences were not due to elevated VLDL production, but rather to lower lipolysis and uptake rates. © 2014 van Schalkwijk et al. Source

van Bochove K.,TNO | van Schalkwijk D.B.,TNO | van Schalkwijk D.B.,The Leiden Amsterdam Center for Drug Research | van Schalkwijk D.B.,The Netherlands Bioinformatics Center | And 6 more authors.
PLoS ONE | Year: 2012

Fibrates lower triglycerides and raise HDL cholesterol in dyslipidemic patients, but show heterogeneous treatment response. We used k-means clustering to identify three representative NMR lipoprotein profiles for 775 subjects from the GOLDN population, and study the response to fenofibrate in corresponding subgroups. The subjects in each subgroup showed differences in conventional lipid characteristics and in presence/absence of cardiovascular risk factors at baseline; there were subgroups with a low, medium and high degree of dyslipidemia. Modeling analysis suggests that the difference between the subgroups with low and medium dyslipidemia is influenced mainly by hepatic uptake dysfunction, while the difference between subgroups with medium and high dyslipidemia is influenced mainly by extrahepatic lipolysis disfunction. The medium and high dyslipidemia subgroups showed a positive, yet distinct lipid response to fenofibrate treatment. When comparing our subgroups to known subgrouping methods, we identified an additional 33% of the population with favorable lipid response to fenofibrate compared to a standard baseline triglyceride cutoff method. Compared to a standard HDL cholesterol cutoff method, the addition was 18%. In conclusion, by using constructing subgroups based on representative lipoprotein profiles, we have identified two subgroups of subjects with positive lipid response to fenofibrate therapy and with different underlying disturbances in lipoprotein metabolism. The total subgroup with positive lipid response to fenofibrate is larger than subgroups identified with baseline triglyceride and HDL cholesterol cutoffs. Source

Berkhout J.,VU University Amsterdam | Berkhout J.,Kluyver Center for Genomics of Industrial Fermentation | Bosdriesz E.,VU University Amsterdam | Bosdriesz E.,The Netherlands Bioinformatics Center | And 11 more authors.
Genetics | Year: 2013

Evolutionary adaptations in metabolic networks are fundamental to evolution of microbial growth. Studies on unneeded-protein synthesis indicate reductions in fitness upon nonfunctional protein synthesis, showing that cell growth is limited by constraints acting on cellular protein content. Here, we present a theory for optimal metabolic enzyme activity when cells are selected for maximal growth rate given such growth-limiting biochemical constraints. We show how optimal enzyme levels can be understood to result from an enzyme benefit minus cost optimization. The constraints we consider originate from different biochemical aspects of microbial growth, such as competition for limiting amounts of ribosomes or RNA polymerases, or limitations in available energy. Enzyme benefit is related to its kinetics and its importance for fitness, while enzyme cost expresses to what extent resource consumption reduces fitness through constraint-induced reductions of other enzyme levels. A metabolic fitness landscape is introduced to define the fitness potential of an enzyme. This concept is related to the selection coefficient of the enzyme and can be expressed in terms of its fitness benefit and cost. © 2013 by the Genetics Society of America. Source

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