The development and initial validation of the Health and Reproductive Survey (HeRS).

BACKGROUND: Due to the diversity in profiles associated with the female reproductive cycle and their potential physiological and psychological effects, monitoring the reproductive status of exercising females is important from a practical and research perspective. Moreover, as physical activity can influence menstrual function, the effects of physical activity energy expenditure on reproductive function should also be considered. AIM: The aim of this study was to develop and establish initial face and content validity of the Health and Reproductive Survey (HeRS) for physically active females, which is a retrospective assessment of menstrual function from menarche (first menstruation) to menopause (cessation of menstruation). METHODS: Face validity was evaluated qualitatively, and the initial content validity was established through a principal component analysis. The face validity process was completed by 26 females aged 19-67 years and the content validity was established through a survey sent to a convenience sample of 392 females, of which 230 females (57.9% and aged 18-49 years) completed the survey. RESULTS: The revisions made following the face validation improved the understanding, flow, and coherence of the survey. The principal component analysis indicated that, at a minimum, the survey measures these constructs: menstrual cessation and associated moderators, athletic participation and performance levels (as associated with menstruation change and the menstrual cycle), age and menstrual cessation, hormonal contraception ("birth control"), and menarche and associated moderators. CONCLUSION: The Health and Reproductive Survey (HeRS) is a partially validated tool that can be used by researchers to characterize the menstrual status of physically active females relative to their physical activity status.

Transcriptional regulatory code of a eukaryotic genome.

DNA-binding transcriptional regulators interpret the genome's regulatory code by binding to specific sequences to induce or repress gene expression. Comparative genomics has recently been used to identify potential cis-regulatory sequences within the yeast genome on the basis of phylogenetic conservation, but this information alone does not reveal if or when transcriptional regulators occupy these binding sites. We have constructed an initial map of yeast's transcriptional regulatory code by identifying the sequence elements that are bound by regulators under various conditions and that are conserved among Saccharomyces species. The organization of regulatory elements in promoters and the environment-dependent use of these elements by regulators are discussed. We find that environment-specific use of regulatory elements predicts mechanistic models for the function of a large population of yeast's transcriptional regulators.

Partial inhibition of integrin alpha(v)beta6 prevents pulmonary fibrosis without exacerbating inflammation.

RATIONALE: Transforming growth factor (TGF)-beta has a central role in driving many of the pathological processes that characterize pulmonary fibrosis. Inhibition of the integrin alpha(v)beta6, a key activator of TGF-beta in lung, is an attractive therapeutic strategy, as it may be possible to inhibit TGF-beta at sites of alpha(v)beta6 up-regulation without affecting other homeostatic roles of TGF-beta. OBJECTIVES: To analyze the expression of alpha(v)beta6 in human pulmonary fibrosis, and to functionally test the efficacy of therapeutic inhibition of alpha(v)beta6-mediated TGF-beta activation in murine bleomycin-induced pulmonary fibrosis. METHODS: Lung biopsies from patients with a diagnosis of systemic sclerosis or idiopathic pulmonary fibrosis were stained for alpha(v)beta6 expression. A range of concentrations of a monoclonal antibody that blocks alpha(v)beta6-mediated TGF-beta activation was evaluated in murine bleomycin-induced lung fibrosis. MEASUREMENTS AND MAIN RESULTS: Alpha(v)beta6 is overexpressed in human lung fibrosis within pneumocytes lining the alveolar ducts and alveoli. In the bleomycin model, alpha(v)beta6 antibody was effective in blocking pulmonary fibrosis. At high doses, there was increased expression of markers of inflammation and macrophage activation, consistent with the effects of TGF-beta inhibition in the lung. Low doses of antibody attenuated collagen expression without increasing alveolar inflammatory cell populations or macrophage activation markers. CONCLUSIONS: Partial inhibition of TGF-beta using alpha(v)beta6 integrin antibodies is effective in blocking murine pulmonary fibrosis without exacerbating inflammation. In addition, the elevated expression of alpha(v)beta6, an activator of the fibrogenic cytokine, TGF-beta, in human pulmonary fibrosis suggests that alpha(v)beta6 monoclonal antibodies could represent a promising new therapeutic strategy for treating pulmonary fibrosis.

Computational discovery of gene modules and regulatory networks.

We describe an algorithm for discovering regulatory networks of gene modules, GRAM (Genetic Regulatory Modules), that combines information from genome-wide location and expression data sets. A gene module is defined as a set of coexpressed genes to which the same set of transcription factors binds. Unlike previous approaches that relied primarily on functional information from expression data, the GRAM algorithm explicitly links genes to the factors that regulate them by incorporating DNA binding data, which provide direct physical evidence of regulatory interactions. We use the GRAM algorithm to describe a genome-wide regulatory network in Saccharomyces cerevisiae using binding information for 106 transcription factors profiled in rich medium conditions data from over 500 expression experiments. We also present a genome-wide location analysis data set for regulators in yeast cells treated with rapamycin, and use the GRAM algorithm to provide biological insights into this regulatory network

Bayesian estimation of transcript levels using a general model of array measurement noise.

Gene arrays demonstrate a promising ability to characterize expression levels across the entire genome but suffer from significant levels of measurement noise. We present a rigorous new approach to estimate transcript levels and ratios from one or more gene array experiments, given a model of measurement noise and available prior information. The Bayesian estimation of array measurements (BEAM) technique provides a principled method to identify changes in expression level, combine repeated measurements, or deal with negative expression level measurements. BEAM is more flexible than existing techniques, because it does not assume a specific functional form for noise and prior models. Instead, it relies on computational techniques that apply to a broad range of models. We use Affymetrix yeast chip data to illustrate the process of developing accurate noise and prior models from existing experimental data. The resulting noise model includes novel features such as heavy-tailed additive noise and a gene-specific bias term. We also verify that the resulting noise and prior models fit data from an Affymetrix human chip set.

Global position and recruitment of HATs and HDACs in the yeast genome.

Chromatin regulators play fundamental roles in the regulation of gene expression and chromosome maintenance, but the regions of the genome where most of these regulators function has not been established. We explored the genome-wide occupancy of four different chromatin regulators encoded in Saccharomyces cerevisiae. The results reveal that the histone acetyltransferases Gcn5 and Esa1 are both generally recruited to the promoters of active protein-coding genes. In contrast, the histone deacetylases Hst1 and Rpd3 are recruited to specific sets of genes associated with distinct cellular functions. Our results provide new insights into the association of histone acetyltransferases and histone deacetylases with the yeast genome, and together with previous studies, suggest how these chromatin regulators are recruited to specific regions of the genome.

Control of pancreas and liver gene expression by HNF transcription factors.

The transcriptional regulatory networks that specify and maintain human tissue diversity are largely uncharted. To gain insight into this circuitry, we used chromatin immunoprecipitation combined with promoter microarrays to identify systematically the genes occupied by the transcriptional regulators HNF1alpha, HNF4alpha, and HNF6, together with RNA polymerase II, in human liver and pancreatic islets. We identified tissue-specific regulatory circuits formed by HNF1alpha, HNF4alpha, and HNF6 with other transcription factors, revealing how these factors function as master regulators of hepatocyte and islet transcription. Our results suggest how misregulation of HNF4alpha can contribute to type 2 diabetes.

Transcriptional regulatory networks in Saccharomyces cerevisiae.

We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.