Biomedical subjects

Hinrich W Göhlmann

Publications and source records attributed to Hinrich W Göhlmann.

3 recordsLinked to original sources

PubMed2005

Molecular profiling of murine sensory neurons in the nodose and dorsal root ganglia labeled from the peritoneal cavity.

Vagal afferent neurons are thought to convey primarily physiological information, whereas spinal afferents transmit noxious signals from the viscera to the central nervous system. To elucidate molecular identities for these different properties, we compared gene expression profiles of neurons located in nodose ganglia (NG) and dorsal root ganglia (DRG) in mice. Intraperitoneal administration of Alexa Fluor-488-conjugated cholera toxin B allowed enrichment for neurons projecting to the viscera. Fluorescent neurons in DRG (from T10 to T13) and NG were isolated using laser-capture microdissection. Gene expression profiles of these afferent neurons, obtained by microarray hybridization, were analyzed using multivariate spectral map analysis, significance analysis of microarrays (SAM) algorithm, and fold-difference filtering. A total of 1,996 genes were differentially expressed in DRG vs. NG, including 41 G protein-coupled receptors and 60 ion channels. Expression profiles obtained on laser-captured neurons were contrasted to those obtained on whole ganglia, demonstrating striking differences and the need for microdissection when studying visceral sensory neurons because of dilution of the signal by somatic sensory neurons. Furthermore, we provide a detailed catalog of all adrenergic and cholinergic, GABA, glutamate, serotonin, and dopamine receptors; voltage-gated potassium, sodium, and calcium channels; and transient receptor potential cation channels present in afferents projecting to the peritoneal cavity. Our genome-wide expression profiling data provide novel insight into molecular signatures that underlie both functional differences and similarities between NG and DRG sensory neurons. Moreover, these findings will offer novel insight into mode of action of pharmacological agents modulating visceral sensation.

Animals↗

PubMed2004

Transcriptional response to corticotropin-releasing factor in AtT-20 cells.

Corticotropin-releasing factor (CRF) plays a central role in the regulation of the hypothalamic-pituitary-adrenal axis, mediating endocrine and behavioral responses to various stressors. Two high-affinity receptors for CRF have been described. Although many of the intracellular signaling pathways activated by CRF have been studied extensively, our knowledge of transcriptional responses downstream of the CRF receptor 1 (CRFR1) is still limited. To elucidate gene networks regulated by CRF and CRFR1, we applied microarray technology to explore transcriptional response to CRF stimulation. Therefore, mouse pituitary-derived AtT-20 cells were exposed continuously to CRF either in the presence or absence of the specific CRFR1 antagonist R121919. Transcriptional responses to different treatments were studied in a time course ranging from 0.5 to 24 h. Microarray data were analyzed using classic microarray data analysis tools such as correspondence factor analysis, cluster analysis, and fold-change filtering. Furthermore, spectral map analysis was applied, a recently introduced unsupervised multivariate analysis method. A broad and transient transcriptional response to CRF was identified that could be blocked by the antagonist. This way, several known CRF-induced target genes and novel CRF responsive genes were identified. These include transcription factors such as cAMP-responsive element modulator (7x increased), secreted peptides such as cholecystokinin (1.5x), and proteins involved in modulating intracellular signaling, such as regulator of G-protein signaling 2 (11x). Up-regulation of many of these genes can be explained as negative feedback, attenuating CRF-activated pathways. In addition, spectral map analysis proved to be a promising new tool for microarray data analysis.

Animals↗

PubMed2003

Graphical exploration of gene expression data: a comparative study of three multivariate methods.

This article describes three multivariate projection methods and compares them for their ability to identify clusters of biological samples and genes using real-life data on gene expression levels of leukemia patients. It is shown that principal component analysis (PCA) has the disadvantage that the resulting principal factors are not very informative, while correspondence factor analysis (CFA) has difficulties interpreting distances between objects. Spectral map analysis (SMA) is introduced as an alternative approach to the analysis of microarray data. Weighted SMA outperforms PCA, and is at least as powerful as CFA, in finding clusters in the samples, as well as identifying genes related to these clusters. SMA addresses the problem of data analysis in microarray experiments in a more appropriate manner than CFA, and allows more flexible weighting to the genes and samples. Proper weighting is important, since it enables less reliable data to be down-weighted and more reliable information to be emphasized.

Biometry↗