MED13-dependent signaling from the heart confers leanness by enhancing metabolism in adipose tissue and liver.

The heart requires a continuous supply of energy but has little capacity for energy storage and thus relies on exogenous metabolic sources. We previously showed that cardiac MED13 modulates systemic energy homeostasis in mice. Here, we sought to define the extra-cardiac tissue(s) that respond to cardiac MED13 signaling. We show that cardiac overexpression of MED13 in transgenic (MED13cTg) mice confers a lean phenotype that is associated with increased lipid uptake, beta-oxidation and mitochondrial content in white adipose tissue (WAT) and liver. Cardiac expression of MED13 decreases metabolic gene expression in the heart but enhances them in WAT. Although exhibiting increased energy expenditure in the fed state, MED13cTg mice metabolically adapt to fasting. Furthermore, MED13cTg hearts oxidize fuel that is readily available, rendering them more efficient in the fed state. Parabiosis experiments in which circulations of wild-type and MED13cTg mice are joined, reveal that circulating factor(s) in MED13cTg mice promote enhanced metabolism and leanness. These findings demonstrate that MED13 acts within the heart to promote systemic energy expenditure in extra-cardiac energy depots and point to an unexplored metabolic communication system between the heart and other tissues.

Ataxia and Purkinje cell degeneration in mice lacking the CAMTA1 transcription factor.

Members of the calmodulin-binding transcription activator (CAMTA) family of proteins function as calcium-sensitive regulators of gene expression in multicellular organisms ranging from plants to humans. Here, we show that global or nervous system deletion of CAMTA1 in mice causes severe ataxia with Purkinje cell degeneration and cerebellar atrophy, partially resembling the consequences of haploinsufficiency of the human CAMTA1 locus. Gene-expression analysis identified a large collection of neuronal genes that were dysregulated in the brains of CAMTA1-mutant mice, and elucidation of a consensus sequence for binding of CAMTA proteins to DNA revealed the association of CAMTA-binding sites with many of these genes. We conclude that CAMTA1 plays an essential role in the control of Purkinje cell function and survival. CAMTA1-mutant mice provide a model to study the molecular mechanisms of neurodegenerative diseases and for screening potential therapeutic interventions for such disorders.

Toward an ontology-based framework for clinical research databases.

Clinical research includes a wide range of study designs from focused observational studies to complex interventional studies with multiple study arms, treatment and assessment events, and specimen procurement procedures. Participant characteristics from case report forms need to be integrated with molecular characteristics from mechanistic experiments on procured specimens. In order to capture and manage this diverse array of data, we have developed the Ontology-Based eXtensible data model (OBX) to serve as a framework for clinical research data in the Immunology Database and Analysis Portal (ImmPort). By designing OBX around the logical structure of the Basic Formal Ontology (BFO) and the Ontology for Biomedical Investigations (OBI), we have found that a relatively simple conceptual model can represent the relatively complex domain of clinical research. In addition, the common framework provided by BFO makes it straightforward to develop data dictionaries based on reference and application ontologies from the OBO Foundry.

Elucidation of seventeen human peripheral blood B-cell subsets and quantification of the tetanus response using a density-based method for the automated identification of cell populations in multidimensional flow cytometry data.

BACKGROUND: Advances in multiparameter flow cytometry (FCM) now allow for the independent detection of larger numbers of fluorochromes on individual cells, generating data with increasingly higher dimensionality. The increased complexity of these data has made it difficult to identify cell populations from high-dimensional FCM data using traditional manual gating strategies based on single-color or two-color displays. METHODS: To address this challenge, we developed a novel program, FLOCK (FLOw Clustering without K), that uses a density-based clustering approach to algorithmically identify biologically relevant cell populations from multiple samples in an unbiased fashion, thereby eliminating operator-dependent variability. RESULTS: FLOCK was used to objectively identify seventeen distinct B-cell subsets in a human peripheral blood sample and to identify and quantify novel plasmablast subsets responding transiently to tetanus and other vaccinations in peripheral blood. FLOCK has been implemented in the publically available Immunology Database and Analysis Portal-ImmPort (http://www.immport.org)-for open use by the immunology research community. CONCLUSIONS: FLOCK is able to identify cell subsets in experiments that use multiparameter FCM through an objective, automated computational approach. The use of algorithms like FLOCK for FCM data analysis obviates the need for subjective and labor-intensive manual gating to identify and quantify cell subsets. Novel populations identified by these computational approaches can serve as hypotheses for further experimental study.

GO-Bayes: Gene Ontology-based overrepresentation analysis using a Bayesian approach.

MOTIVATION: A typical approach for the interpretation of high-throughput experiments, such as gene expression microarrays, is to produce groups of genes based on certain criteria (e.g. genes that are differentially expressed). To gain more mechanistic insights into the underlying biology, overrepresentation analysis (ORA) is often conducted to investigate whether gene sets associated with particular biological functions, for example, as represented by Gene Ontology (GO) annotations, are statistically overrepresented in the identified gene groups. However, the standard ORA, which is based on the hypergeometric test, analyzes each GO term in isolation and does not take into account the dependence structure of the GO-term hierarchy. RESULTS: We have developed a Bayesian approach (GO-Bayes) to measure overrepresentation of GO terms that incorporates the GO dependence structure by taking into account evidence not only from individual GO terms, but also from their related terms (i.e. parents, children, siblings, etc.). The Bayesian framework borrows information across related GO terms to strengthen the detection of overrepresentation signals. As a result, this method tends to identify sets of closely related GO terms rather than individual isolated GO terms. The advantage of the GO-Bayes approach is demonstrated with a simulation study and an application example.

Identifying Differentially Expressed Genes based on probe level data for GeneChip arrays.

We propose a new method based on probe-level data (PLIDEG) to filter differentially expressed genes from non-differentially expressed genes. We compare this new method with others based on expression values by using two spikein data sets. With the extra information provided by probe level data, PLIDEG not only controls type I error, but also increases the power of detecting DEGs, simultaneously. Therefore, PLIDEG can efficiently separate DEGs and non-DEGs without requiring the estimation of the number of non-DEGs. Based on theoretical analysis and results from application to real microarray data, we confirm these good features of this new method.

A comprehensive survey of DNA-binding transcription factor gene expression in human fetal and adult organs.

A global survey of RNA from 14 fetal and 12 adult human organs by RT-PCR determined the expression patterns of 790 genes encoding DNA-binding transcription factors. The data can be sorted to identify sets of transcription factors with expression relatively restricted to a given organ or to particular organ groups. These data are a resource to help define the spectrum of transcription factor control, contribute to the elucidation of transcription factor cascades responsible for the development and maintenance of each organ, and provide a baseline to study the effects of disease or developmental defects.