Deducing corticotropin-releasing hormone receptor type 1 signaling networks from gene expression data by usage of genetic algorithms and graphical Gaussian models

<p>Abstract</p> <p>Background</p> <p>Dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis is a hallmark of complex and multifactorial psychiatric diseases such as anxiety and mood disorders. About 50-60% of patients with major depression show HPA axis dysfunction, i.e. hyperactivity and impaired negative feedback regulation. The neuropeptide corticotropin-releasing hormone (CRH) and its receptor type 1 (CRHR1) are key regulators of this neuroendocrine stress axis. Therefore, we analyzed CRH/CRHR1-dependent gene expression data obtained from the pituitary corticotrope cell line AtT-20, a well-established <it>in vitro </it>model for CRHR1-mediated signal transduction. To extract significantly regulated genes from a genome-wide microarray data set and to deduce underlying CRHR1-dependent signaling networks, we combined supervised and unsupervised algorithms.</p> <p>Results</p> <p>We present an efficient variable selection strategy by consecutively applying univariate as well as multivariate methods followed by graphical models. First, feature preselection was used to exclude genes not differentially regulated over time from the dataset. For multivariate variable selection a maximum likelihood (MLHD) discriminant function within GALGO, an R package based on a genetic algorithm (GA), was chosen. The topmost genes representing major nodes in the expression network were ranked to find highly separating candidate genes. By using groups of five genes (chromosome size) in the discriminant function and repeating the genetic algorithm separately four times we found eleven genes occurring at least in three of the top ranked result lists of the four repetitions. In addition, we compared the results of GA/MLHD with the alternative optimization algorithms greedy selection and simulated annealing as well as with the state-of-the-art method random forest. In every case we obtained a clear overlap of the selected genes independently confirming the results of MLHD in combination with a genetic algorithm.</p> <p>With two unsupervised algorithms, principal component analysis and graphical Gaussian models, putative interactions of the candidate genes were determined and reconstructed by literature mining. Differential regulation of six candidate genes was validated by qRT-PCR.</p> <p>Conclusions</p> <p>The combination of supervised and unsupervised algorithms in this study allowed extracting a small subset of meaningful candidate genes from the genome-wide expression data set. Thereby, variable selection using different optimization algorithms based on linear classifiers as well as the nonlinear random forest method resulted in congruent candidate genes. The calculated interacting network connecting these new target genes was bioinformatically mapped to known CRHR1-dependent signaling pathways. Additionally, the differential expression of the identified target genes was confirmed experimentally.</p

TERENO: German network of terrestrial environmental observatories

Central elements of the TERENO network are “terrestrial observatories” at the catchment scale which were selected in climate sensitive regions of Germany for the regional analyses of climate change impacts. Within these observatories small scale research facilities and test areas are placed in order to accomplish energy, water, carbon and nutrient process studies across the different compartments of the terrestrial environment. Following a hierarchical scaling approach (point-plot-field) these detailed information and the gained knowledge will be transferred to the regional scale using integrated modelling approaches. Furthermore, existing research stations are enhanced and embedded within the observatories. In addition, mobile measurement platforms enable monitoring of dynamic processes at the local scale up to the determination of spatial pattern at the regional scale are applied within TERENO

Biophysical Characterization of Pro-apoptotic BimBH3 Peptides Reveals an Unexpected Capacity for Self-Association

Bcl-2 proteins orchestrate the mitochondrial pathway of apoptosis, pivotal for cell death. Yet, the structural details of the conformational changes of pro- and antiapoptotic proteins and their interactions remain unclear. Pulse dipolar spectroscopy (double electron-electron resonance [DEER], also known as PELDOR) in combination with spin-labeled apoptotic Bcl-2 proteins unveils conformational changes and interactions of each protein player via detection of intra- and inter-protein distances. Here, we present the synthesis and characterization of pro-apoptotic BimBH3 peptides of different lengths carrying cysteines for labeling with nitroxide or gadolinium spin probes. We show by DEER that the length of the peptides modulates their homo-interactions in the absence of other Bcl-2 proteins and solve by X-ray crystallography the structure of a BimBH3 tetramer, revealing the molecular details of the inter-peptide interactions. Finally, we prove that using orthogonal labels and three-channel DEER we can disentangle the Bim-Bim, Bcl-xL-Bcl-xL, and Bim-Bcl-xL interactions in a simplified interactome.This work was funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—EXC-2033—Projektnummer 390677874, the DFG Priority Program SPP1601 “New Frontiers in Sensitivity in EPR Spectroscopy” (to E.B.), DFG BO 3000/5-1 (to E.B.), SFB958 – Z04 (to E.B.), DFG grant INST 130/972-1 FUGG (to E.B.). P.E.C. is supported by an Australian NHMRC fellowship (1079700

Molecular MRI in the Earth's Magnetic Field Using Continuous Hyperpolarization of a Biomolecule in Water

In this work, we illustrate a method to continuously hyperpolarize a biomolecule, nicotinamide, in water using parahydrogen and signal amplification by reversible exchange (SABRE). Building on the preparation procedure described recently by Truong et al. [ J. Phys. Chem. B, 2014, 118, 13882-13889 ], aqueous solutions of nicotinamide and an Ir-IMes catalyst were prepared for low-field NMR and MRI. The 1H-polarization was continuously renewed and monitored by NMR experiments at 5.9 mT for more than 1000 s. The polarization achieved corresponds to that induced by a 46 T magnet (P = 1.6 × 10-4) or an enhancement of 104. The polarization persisted, although reduced, if cell culture medium (DPBS with Ca2+ and Mg2+) or human cells (HL-60) were added, but was no longer observable after the addition of human blood. Using a portable MRI unit, fast 1H-MRI was enabled by cycling the magnetic field between 5 mT and the Earth's field for hyperpolarization and imaging, respectively. A model describing the underlying spin physics was developed that revealed a polarization pattern depending on both contact time and magnetic field. Furthermore, the model predicts an opposite phase of the dihydrogen and substrate signal after one exchange, which is likely to result in the cancelation of some signal at low field