Biomedical subjects

Joachim Theilhaber

Publications and source records attributed to Joachim Theilhaber.

3 recordsLinked to original sources

PubMed2005

Identification of a molecular signature of sarcopenia.

Investigating the molecular mechanisms underlying sarcopenia in humans with the use of microarrays has been complicated by low sample size and the variability inherent in human gene expression profiles. We have conducted a study using Affymetrix GeneChips to identify a molecular signature of aged skeletal muscle. The molecular signature was defined as the set of expressed genes that best distinguished the vastus lateralis muscle of young (n = 10) and older (n = 12) male subjects, when a k-nearest neighbor supervised classification method was used in conjunction with a signal-to-noise ratio gene selection method and a holdout cross-validation procedure. The age-specific expression signature was comprised of 45 genes; 27 were upregulated and 18 were downregulated. This signature also correctly classified 75% of the muscle samples from young and older subjects published by an independent laboratory, based on their expression profiles. The signature revealed increased expression of several genes involved in mediating cellular responses to inflammation and apoptosis, including complement component C1QA, Galectin-1, C/EBP-beta, and FOXO3A, among others. The increased expressions of genes that regulate pre-mRNA splicing, localization, and modification of RNA comprise markers of the aging signature. Downregulated genes in the signature were the glutamine transporter SLC38A1, a TRAF-6 inhibitory zinc finger protein, and membrane-bound transcription factor protease S2P, among others. The sarcopenia signature developed here will be useful as a molecular model to judge the effectiveness of exercise and other therapeutic treatments aimed at ameliorating the effects of muscle loss associated with aging.

Adult↗

PubMed2004

GECKO: a complete large-scale gene expression analysis platform.

BACKGROUND: Gecko (Gene Expression: Computation and Knowledge Organization) is a complete, high-capacity centralized gene expression analysis system, developed in response to the needs of a distributed user community. RESULTS: Based on a client-server architecture, with a centralized repository of typically many tens of thousands of Affymetrix scans, Gecko includes automatic processing pipelines for uploading data from remote sites, a data base, a computational engine implementing approximately 50 different analysis tools, and a client application. Among available analysis tools are clustering methods, principal component analysis, supervised classification including feature selection and cross-validation, multi-factorial ANOVA, statistical contrast calculations, and various post-processing tools for extracting data at given error rates or significance levels. On account of its open architecture, Gecko also allows for the integration of new algorithms. The Gecko framework is very general: non-Affymetrix and non-gene expression data can be analyzed as well. A unique feature of the Gecko architecture is the concept of the Analysis Tree (actually, a directed acyclic graph), in which all successive results in ongoing analyses are saved. This approach has proven invaluable in allowing a large (approximately 100 users) and distributed community to share results, and to repeatedly return over a span of years to older and potentially very complex analyses of gene expression data. CONCLUSIONS: The Gecko system is being made publicly available as free software http://sourceforge.net/projects/geckoe. In totality or in parts, the Gecko framework should prove useful to users and system developers with a broad range of analysis needs.

Carcinoma↗

PubMed2002

Finding genes in the C2C12 osteogenic pathway by k-nearest-neighbor classification of expression data.

A supervised classification scheme for analyzing microarray expression data, based on the k-nearest-neighbor method coupled to noise-reduction filters, has been used to find genes involved in the osteogenic pathway of the mouse C2C12 cell line studied here as a model for in vivo osteogenesis. The scheme uses as input a training set embodying expert biological knowledge, and provides internal estimates of its own misclassification errors, which furthermore enables systematic optimization of the classifier parameters. On the basis of the C2C12-generated expression data set with 34,130 expression profiles across 2 time courses, each comprised of 6 points, and a training set containing known members of the osteogenic, myoblastic, and adipocytic pathways, 176 new genes in addition to 28 originally in the training set are selected as relevant to osteogenesis. For this selection, the estimated sensitivity is 42% and the posterior false-positive rate (fraction of candidates that are spurious) is 12%. The corresponding sensitivity and false-positive rate for detection of myoblastic genes are 9% and 31%, respectively, and only 4% and approximately 100%, respectively, for adipocytic genes, in accordance with an experimental design that predominantly stimulated the osteogenic pathway. Validation of this selection is provided by examining expression of the genes in an independent biological assay involving mouse calvaria (skull bone) primary cell cultures, in which a large fraction of the 176 genes are seen to be strongly regulated, as well as by case-by-case analysis of the genes on the basis of expert domain knowledge. The methodology should be generalizable to any situation in which enough a priori biological knowledge exists to define a training set.

Animals↗