Systems biology strategy to study lipotoxicity and the metabolic syndrome

Systems biology views and studies the biological systems in the context of complex interactions between their building blocks and processes. Given its multi-level complexity, metabolic syndrome (MetS) makes a strong case for adopting the systems biology approach. Despite many MetS traits being highly heritable, it is becoming evident that the genetic contribution to these traits is mediated via gene–gene and gene–environment interactions across several spatial and temporal scales, and that some of these traits such as lipotoxicity may even be a product of long-term dynamic changes of the underlying genetic and molecular networks. This presents several conceptual as well as methodological challenges and may demand a paradigm shift in how we study the undeniably strong genetic component of complex diseases such as MetS. The argument is made here that for adopting systems biology approaches to MetS an integrative framework is needed which glues the biological processes of MetS with specific physiological mechanisms and principles and that lipotoxicity is one such framework. The metabolic phenotypes, molecular and genetic networks can be modeled within the context of such integrative framework and the underlying physiology.     

Kausal-analytische Untersuchungen Über die Herkunft des Chromatins

Ohne Zusammenfassung     

The P25 component of Galleria silk

The water-insoluble core of lepidopteran silk is composed of four major proteins, but only three genes have been identified. This study demonstrates that the 29- and 30-kDa components of Galleria mellonella silk are derived from a single gene designated P25. The gene is expressed exclusively in the posterior section of the silk glands as a 2-kb mRNA, which accumulates in the feeding larvae and declines at molting. The mRNA encodes a peptide of 24 864 Da that exhibits 51% identity with the putative product of the P25 gene of Bombyx. The conservation of several amino acid stretches, including the relative positions of all 8 cysteines in the mature polypeptide, implies that the P25 proteins play similar, and apparently significant roles in silk formation in the two species. A Galleria P25 cDNA yields a peptide of about 25?kDa when translated in vitro; the 29- and 30-kDa forms present in the silk are derived from this primary translation product by differential glycosylation.     

Functional prediction of unidentified lipids using supervised classifiers

Mass spectrometry (MS)-based metabolomics studies often require handling of both identified and unidentified metabolite data. In order to avoid bias in data interpretation, it would be of advantage for the data analysis to include all available data. A practical challenge in exploratory metabolomics analysis is therefore how to interpret the changes related to unidentified peaks. In this paper, we address the challenge by predicting the class membership of unknown peaks by applying and comparing multiple supervised classifiers to selected lipidomics datasets. The employed classifiers include k-nearest neighbours (k-NN), support vector machines (SVM), partial least squares and discriminant analysis (PLS-DA) and Naive Bayes methods which are known to be effective and efficient in predicting the labels for unseen data. Here, the class label predictions are sought for unidentified lipid profiles coming from high throughput global screening in Ultra Performance Liquid Chromatography Mass Spectrometry (UPLC^TM/MS) experimental setup. Our investigation reveals that k-NN and SVM classifiers outperform both PLS-DA and Naive Bayes classifiers. Naive Bayes classifier perform poorly among all models and this observation seems logical as lipids are highly co-regulated and do not respect Naive Bayes assumptions of features being conditionally independent given the class. Common label predictions from k-NN and SVM can serve as a good starting point to explore full data and thereby facilitating exploratory studies where label information is critical for the data interpretation.     

Application of Chaos Theory in Analyzing the Cardiac Rhythm in Healthy Subjects at Different Sleep Stages

The changes in the chaotic element of the cardiac rhythm (CR) were quantitated at different sleep stages by calculating the correlation dimension (D2) in 26 healthy subjects of both sexes (mean age 29.2 years), including 7 trained and 19 untrained subjects. Three untrained subjects took part in tests with autonomic nervous system blockers (atropine and propranolol). The study demonstrated a correlation between the changes in D2 at different sleep stages and the level of the autonomic regulation of CR. As the influence of the parasympathetic system on CR increased from one stage of slow wave sleep to another, D2 increased; during rapid eye movement (REM) sleep, this influence weakened and D2 decreased. The character of changes differed in the trained and untrained subjects and depended on the initial level of the autonomic regulation of CR. In the trained subjects, characterized by predominance of the parasympathetic regulation of CR, the initial and subsequent D2 values were higher than in the untrained subjects. Both during wakefulness and at all stages of sleep, D2 increased when the sympathetic regulation of CR was blocked, decreased when the parasympathetic regulation was blocked, and reached the lowest level when both of them were blocked. This showed that the chaotic element of CR, expressed numerically by D2, depends on the regulating effects of the autonomic nervous system.