Jaap Keijer

Bio: Jaap Keijer is an academic researcher from Wageningen University and Research Centre. The author has contributed to research in topics: Adipose tissue & White adipose tissue. The author has an hindex of 51, co-authored 247 publications receiving 10039 citations. Previous affiliations of Jaap Keijer include Aix-Marseille University & University of Amsterdam.

Diet-Independent Correlations between Bacteria and Dysfunction of Gut, Adipose Tissue, and Liver: A Comprehensive Microbiota Analysis in Feces and Mucosa of the Ileum and Colon in Obese Mice with NAFLD.

Abstract: Development of non-alcoholic fatty liver disease (NAFLD) is linked to obesity, adipose tissue inflammation, and gut dysfunction, all of which depend on diet. So far, studies have mainly focused on diet-related fecal microbiota changes, but other compartments may be more informative on host health. We present a first systematic analysis of microbiota changes in the ileum and colon using multiple diets and investigating both fecal and mucosal samples. Ldlr−/−.Leiden mice received one of three different energy-dense (ED)-diets (n = 15/group) for 15 weeks. All of the ED diets induced obesity and metabolic risk factors, altered short-chain fatty acids (SCFA), and increased gut permeability and NAFLD to various extents. ED diets reduced the diversity of high-abundant bacteria and increased the diversity of low-abundant bacteria in all of the gut compartments. The ED groups showed highly variable, partially overlapping microbiota compositions that differed significantly from chow. Correlation analyses demonstrated that (1) specific groups of bacteria correlate with metabolic risk factors, organ dysfunction, and NAFLD endpoints, (2) colon mucosa had greater predictive value than other compartments, (3) correlating bacteria differed per compartment, and (4) some bacteria correlated with plasma SCFA levels. In conclusion, this comprehensive microbiota analysis demonstrates correlations between the microbiota and dysfunctions of gut, adipose tissue, and liver, independent of a specific disease-inducing diet.

Tissue Distribution of Quercetin in Rats and Pigs

Abstract: Quercetin is a dietary polyphenolic compound with potentially beneficial effects on health. Claims that quercetin has biological effects are based mainly on in vitro studies with quercetin aglycone. However, quercetin is rapidly metabolized, and we have little knowledge of its availability to tissues. To assess the long-term tissue distribution of quercetin, 2 groups of rats were given a 0.1 or 1% quercetin diet [∼50 or 500 mg/kg body weight (wt)] for 11 wk. In addition, a 3-d study was done with pigs fed a diet containing 500 mg quercetin/kg body wt. Tissue concentrations of quercetin and quercetin metabolites were analyzed with an optimized extraction method. Quercetin and quercetin metabolites were widely distributed in rat tissues, with the highest concentrations in lungs (3.98 and 15.3 nmol/g tissue for the 0.1 and 1% quercetin diet, respectively) and the lowest in brain, white fat, and spleen. In the short-term pig study, liver (5.87 nmol/g tissue) and kidney (2.51 nmol/g tissue) contained high concentrations of quercetin and quercetin metabolites, whereas brain, heart, and spleen had low concentrations. These studies have for the first time identified target tissues of quercetin, which may help to understand its mechanisms of action in vivo. © 2005 American Society for Nutritional Sciences.

Polyunsaturated fatty acids of marine origin upregulate mitochondrial biogenesis and induce beta-oxidation in white fat.

Abstract: Intake of n-3 polyunsaturated fatty acids reduces adipose tissue mass, preferentially in the abdomen. The more pronounced effect of marine-derived eicosapentaenoic (EPA) and docosahexaenoic (DHA) acids on adiposity, compared with their precursor α-linolenic acid, may be mediated by changes in gene expression and metabolism in white fat. The effects of EPA/DHA concentrate (6% EPA, 51% DHA) admixed to form two types of high-fat diet were studied in C57BL/6J mice. Oligonucleotide microarrays, cDNA PCR subtraction and quantitative real-time RT-PCR were used to characterise gene expression. Mitochondrial proteins were quantified using immunoblots. Fatty acid oxidation and synthesis were measured in adipose tissue fragments. Expression screens revealed upregulation of genes for mitochondrial proteins, predominantly in epididymal fat when EPA/DHA concentrate was admixed to a semisynthetic high-fat diet rich in α-linolenic acid. This was associated with a three-fold stimulation of the expression of genes encoding regulatory factors for mitochondrial biogenesis and oxidative metabolism (peroxisome proliferator-activated receptor gamma coactivator 1 alpha [Ppargc1a, also known as Pgc1α] and nuclear respiratory factor-1 [Nrf1] respectively). Expression of genes for carnitine palmitoyltransferase 1A and fatty acid oxidation was increased in epididymal but not subcutaneous fat. In the former depot, lipogenesis was depressed. Similar changes in adipose gene expression were detected after replacement of as little as 15% of lipids in the composite high-fat diet with EPA/DHA concentrate, while the development of obesity was reduced. The expression of Ppargc1a and Nrf1 was also stimulated by n-3 polyunsaturated fatty acids in 3T3-L1 cells. The anti-adipogenic effect of EPA/DHA may involve a metabolic switch in adipocytes that includes enhancement of β-oxidation and upregulation of mitochondrial biogenesis.

The secretory function of adipocytes in the physiology of white adipose tissue

Abstract: White adipose tissue, previously regarded as a passive lipid storage site, is now viewed as a dynamic tissue. It has the capacity to actively communicate by sending and receiving different types of signals. An overview of these signals, the external modulators that affect adipose tissue and the secreted signaling molecules, the adipokines, is presented. The secretory function is highlighted in relation to energy metabolism, inflammation and the extracellular matrix and placed in the context of adipose tissue biology. We observe that the endocrine function of adipocytes receives much attention, while its paracrine and autocrine functions are underestimated. Also, we provide examples that species specificity should not be neglected. We conclude that adipose tissue primarily is an energy storage organ, well supported by its secretory function.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Brown Adipose Tissue: Function and Physiological Significance

Abstract: Cannon, Barbara, and Jan Nedergaard. Brown Adipose Tissue: Function and Physiological Significance. Physiol Rev 84: 277–359, 2004; 10.1152/physrev.00015.2003.—The function of brown adipose tissue i...

Conditional variable importance for random forests

Abstract: Random forests are becoming increasingly popular in many scientific fields because they can cope with "small n large p" problems, complex interactions and even highly correlated predictor variables. Their variable importance measures have recently been suggested as screening tools for, e.g., gene expression studies. However, these variable importance measures show a bias towards correlated predictor variables. We identify two mechanisms responsible for this finding: (i) A preference for the selection of correlated predictors in the tree building process and (ii) an additional advantage for correlated predictor variables induced by the unconditional permutation scheme that is employed in the computation of the variable importance measure. Based on these considerations we develop a new, conditional permutation scheme for the computation of the variable importance measure. The resulting conditional variable importance reflects the true impact of each predictor variable more reliably than the original marginal approach.

Macrophages, Inflammation, and Insulin Resistance

Abstract: Obesity induces an insulin-resistant state in adipose tissue, liver, and muscle and is a strong risk factor for the development of type 2 diabetes mellitus. Insulin resistance in the setting of obesity results from a combination of altered functions of insulin target cells and the accumulation of macrophages that secrete proinflammatory mediators. At the molecular level, insulin resistance is promoted by a transition in macrophage polarization from an alternative M2 activation state maintained by STAT6 and PPARs to a classical M1 activation state driven by NF-kappaB, AP1, and other signal-dependent transcription factors that play crucial roles in innate immunity. Strategies focused on inhibiting the inflammation/insulin resistance axis that otherwise preserve essential innate immune functions may hold promise for therapeutic intervention.

An Introduction to Recursive Partitioning: Rationale, Application and Characteristics of Classification and Regression Trees, Bagging and Random Forests

Abstract: Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and bioinformatics within the past few years. High-dimensional problems are common not only in genetics, but also in some areas of psychological research, where only a few subjects can be measured because of time or cost constraints, yet a large amount of data is generated for each subject. Random forests have been shown to achieve a high prediction accuracy in such applications and to provide descriptive variable importance measures reflecting the impact of each variable in both main effects and interactions. The aim of this work is to introduce the principles of the standard recursive partitioning methods as well as recent methodological improvements, to illustrate their usage for low and high-dimensional data exploration, but also to point out limitations of the methods and potential pitfalls in their practical application. Application of the methods is illustrated with freely available implementations in the R system for statistical computing.