Longitudinal Assessment of In Vivo Bone Dynamics in a Mouse Tail Model of Postmenopausal Osteoporosis

Recently, it has been shown that transient bone biology can be observed in vivo using time-lapse micro-computed tomography (μCT) in the mouse tail bone. Nevertheless, in order for the mouse tail bone to be a model for human disease, the hallmarks of any disease must be mimicked. The aim of this study was to investigate whether postmenopausal osteoporosis could be modeled in caudal vertebrae of C57Bl/6mice, considering static and dynamic bone morphometry as well as mechanical properties, and to describe temporal changes in bone remodeling rates. Twenty C57Bl/6mice were ovariectomized (OVX, n=11) or sham-operated (SHM, n=9) and monitored with in vivo μCT on the day of surgery and every 2weeks after, up to 12weeks. There was a significant decrease in bone volume fraction for OVX (−35%) compared to SHM (+16%) in trabecular bone (P<0.001). For OVX, high-turnover bone loss was observed, with the bone resorption rate exceeding the bone formation rate (P<0.001). Furthermore there was a significant decrease in whole-bone stiffness for OVX (−16%) compared to SHM (+11%, P<0.001). From these results we conclude that the mouse tail vertebra mimics postmenopausal bone loss with respect to these parameters and therefore might be a suitable model for postmenopausal osteoporosis. When evaluating temporal changes in remodeling rates, we found that OVX caused an immediate increase in bone resorption rate (P<0.001) and a delayed increase in bone formation rate (P<0.001). Monitoring transient bone biology is a promising method for future researc

Psychophysical responses to a speech stressor: Correlation of plasma beta-endorphin levels at rest and after psychological stress with thermally measured pain threshold in patients with coronary artery disease

OBJECTIVES: We tested the hypothesis that psychological stress alters plasma levels of opioid peptides and that these plasma levels are related to pain perception in patients with coronary artery disease. BACKGROUND: Public speaking psychological stress has previously been shown to be associated with silent ischemia. METHODS: After instrumentation and a 30-min rest period, venous blood samples for beta-endorphin were obtained before and immediately after psychological stress in 20 patients with coronary artery disease. Pain threshold was then assessed using a thermal probe technique at baseline and immediately after stress. Patients gave three brief speeches lasting a total of 15 min about real-life hassle situations. RESULTS: Psychological stress significantly increases plasma beta-endorphin levels (4.3 +/- 0.9 pmol/liter [mean +/- SE] at rest to 8.3 +/- 2 pmol/liter after stress, p < 0.05). There was a significant positive correlation between pain threshold and beta-endorphin levels after stress (r = 0.577, p = 0.008). This significant positive correlation was still present while rest blood pressure and change in blood pressure during stress were controlled for by analysis of covariance techniques. CONCLUSIONS: In patients with coronary artery disease and exercise-induced ischemia, public speaking produces psychological stress manifested by increased cardiovascular reactivity and causes an increase in plasma beta-endorphin levels that is significantly correlated with pain thresholds. These findings may explain the predominance of silent ischemia during psychological stress in patients with coronary artery disease

Conformational Polymorphism of cRNA of T-Cell-Receptor Genes as a Clone-Specific Molecular Marker for Cutaneous Lymphoma

A novel molecular assay for the detection and characterization of monoclonal lymphoid populations in clinical specimens was developed. The assay is based on the principle that upon non-denaturing polyacrylamide gel electrophoresis RNA molecules separate into several metastable conformational forms. These conformational polymorphisms strictly depend on the nucleotide sequence of the individual molecule. Using DNA from formalin-fixed, paraffin-embedded tissue of patients with mycosis fungoides, highly variable junctional sequences of rearranged T-cell receptor gamma genes were amplified by polymerase chain reaction. Subsequently, the polymerase chain reactions products were transcribed into complementary RNA and analyzed by non-denaturing polyacrylamide gel electrophoresis. In clinical specimens with a monoclonal lymphoid population, a clone-specific pattern of bands was identified representing conformational polymorphisms of cRNA molecules of rearranged T-cell receptor gamma genes of the predominant lymphoid clone. Three biopsies from one patient taken from different sites of the body over 3 years yielded an identical pattern of bands. This methodology provides a novel and rapid tool for the molecular identification and characterization of clonal lymphoid populations in clinical specimens. It is likely to be of special value for studies on the clonal evolution of lymphoid disorders of the skin

Inference and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large Phylogenies

Changes in transcriptional regulatory networks can significantly contribute to species evolution and adaptation. However, identification of genome-scale regulatory networks is an open challenge, especially in non-model organisms. Here, we introduce multi-species regulatory network learning (MRTLE), a computational approach that uses phylogenetic structure, sequence-specific motifs, and transcriptomic data, to infer the regulatory networks in different species. Using simulated data from known networks and transcriptomic data from six divergent yeasts, we demonstrate that MRTLE predicts networks with greater accuracy than existing methods because it incorporates phylogenetic information. We used MRTLE to infer the structure of the transcriptional networks that control the osmotic stress responses of divergent, non-model yeast species and then validated our predictions experimentally. Interrogating these networks reveals that gene duplication promotes network divergence across evolution. Taken together, our approach facilitates study of regulatory network evolutionary dynamics across multiple poorly studied species. Keywords: regulatory networks; network inference; evolution of gene regulatory networks; evolution of stress response; yeast; probabilistic graphical model; phylogeny; comparative functional genomicsNational Science Foundation (U.S.) (Grant DBI-1350677)National Institutes of Health (U.S.) (Grant R01CA119176-01)National Institutes of Health (U.S.) (Grant DP1OD003958-01

Population sequencing data reveal a compendium of mutational processes in human germline

Mechanistic processes underlying human germline mutations remain largely unknown.Variation in mutation rate and spectra along the genome is informative about the biological mechanisms. We statistically decompose this variation into separate processes using a blind source separation technique. The analysis of a large-scale whole genome sequencing dataset (TOPMed) reveals nine processes that explain the variation in mutation properties between loci. Seven of these processes lend themselves to a biological interpretation. One process is driven by bulky DNA lesions that resolve asymmetrically with respect to transcription and replication. Two processes independently track direction of replication fork and replication timing. We identify a mutagenic effect of active demethylation primarily acting in regulatory regions. We also demonstrate that a recently discovered mutagenic process specific to oocytes can be localized solely from population sequencing data. This process is spread across all chromosomes and is highly asymmetric with respect to the direction of transcription, suggesting a major role of DNA damage

The Molecular Fingerprint of Fluorescent Natural Organic Matter Offers Insight into Biogeochemical Sources and Diagenetic State

Investigating the biogeochemistry of dissolved organic matter (DOM) requires the synthesis of data from several complementary analytical techniques. The traditional approach to data synthesis is to search for correlations between measurements made on the same sample using different instruments. In contrast, data fusion simultaneously decomposes data from multiple instruments into the underlying shared and unshared components. Here, Advanced Coupled Matrix and Tensor Factorization (ACMTF) was used to identify the molecular fingerprint of DOM fluorescence fractions in Arctic fjords. ACMTF explained 99.84% of the variability with six fully shared components. Individual molecular formulas were linked to multiple fluorescencecomponents and vice versa. Molecular fingerprints differed in diversity and oceanographic patterns, suggesting a link to the biogeochemical sources and diagenetic state of DOM. The fingerprints obtained through ACMTF were more specific compared to traditional correlation analysis and yielded greater compositional insight. Multivariate data fusion aligns extremely complex, heterogeneous DOM data sets and thus facilitates a more holistic understanding of DOM biogeochemistry