The Relationship Between Sexually Coercive Experience Frequency, Coping, Social Support and Sexual and Mental Health in Adult Women

poster abstractIntroduction: Existing literature separately identifies social support and coping methods as mediating influences between sexual coercion and adverse health outcomes, yet few empirical studies actually evaluate their influence in the same model. The objective of this study was to analyze how adult women’s coping methods and social support jointly mediate the impact of sexually coercive experience on sexual and mental health. Methods: Data are drawn from a larger internet-based, cross-sectional survey examining adult men’s and women’s health and life experiences. For the current study, we retained all female participants (N=113). Structural equation modelling (SEM) (Stata, v. 22; all p<.05) analyzed the hypothesized structural relationships between coping (adaptive and maladaptive), social support (subjective and emotional), sexual coercion, sexual health (sexual openness, sexual anxiety, sexual esteem, and sexual entitlement) and mental health (depression, self-esteem, and anxiety). Results: More frequent sexual coercion predicted higher maladaptive coping (β = .364). Higher levels of maladaptive coping were associated with higher levels of depression (β = .199), anxiety (β = .393), sexual anxiety (β = .346), and sexual openness (β = .251). Additionally, higher levels of maladaptive coping were associated with lower self-esteem (β = -.226). Adaptive coping and social support were not associated with sexual coercion. Conclusion: Adult women’s sexually coercive experiences impact sexual and mental health indirectly through maladaptive coping, but not through adaptive coping or any social support. Our data raise the possibility that maladaptive coping could be an important catalyst for poor mental and sexual health outcomes following a sexually coercive experience. From an education and policy perspective, this means that a focus on reducing maladaptive coping methods may increase mental and sexual health and reduce the likelihood of accruing more sexually coercive experiences

Relationship length and repeated experiences of sexual coercion within adolescent women's romantic relationships

Indiana University-Purdue University Indianapolis (IUPUI)Sexual coercion is a prevalent and problematic aspect of adolescent women’s sexual experiences, with nationally representative data reporting that 15% of adolescent women were forced by a romantic partner to do sexual things they did not want to do in the past year. However, little is known about how the length of a given adolescent relationship may impact ongoing instances of sexual coercion, and what impact these repeated instances have on the emotional and behavioral characteristics of a given relationship. Accordingly, the current study examines the impact of relationship length on relationship attributes and behaviors within adolescent women’s romantic relationships with repeated experiences of sexual coercion and compares these associations between age groups. Data for the current study were drawn from a larger, longitudinal cohort study (N = 385); utilizing quarterly interviews (N = 5151) that were administered from 1999-2009. Relationship timing of initial and repeat experiences of sexual coercion are discussed. Specifically, our findings suggest that within relationships with repeat experiences of sexual coercion, longer relationship length decreases sexual satisfaction and condom use, while simultaneously increasing vaginal intercourse and the odds of acquiring a sexually transmitted infection

Genetic influences on translation in yeast

Heritable differences in gene expression between individuals are an important source of phenotypic variation. The question of how closely the effects of genetic variation on protein levels mirror those on mRNA levels remains open. Here, we addressed this question by using ribosome profiling to examine how genetic differences between two strains of the yeast S. cerevisiae affect translation. Strain differences in translation were observed for hundreds of genes. Allele specific measurements in the diploid hybrid between the two strains revealed roughly half as many cis-acting effects on translation as were observed for mRNA levels. In both the parents and the hybrid, most effects on translation were of small magnitude, such that the direction of an mRNA difference was typically reflected in a concordant footprint difference. The relative importance of cis and trans acting variation on footprint levels was similar to that for mRNA levels. There was a tendency for translation to cause larger footprint differences than expected given the respective mRNA differences. This is in contrast to translational differences between yeast species that have been reported to more often oppose than reinforce mRNA differences. Finally, we catalogued instances of premature translation termination in the two yeast strains and also found several instances where erroneous reference gene annotations lead to apparent nonsense mutations that in fact reside outside of the translated gene body. Overall, genetic influences on translation subtly modulate gene expression differences, and translation does not create strong discrepancies between genetic influences on mRNA and protein levels

A systems-level analysis of perfect adaptation in yeast osmoregulation

available in PMC 2011 June 7.Negative feedback can serve many different cellular functions, including noise reduction in transcriptional networks and the creation of circadian oscillations. However, only one special type of negative feedback (“integral feedback”) ensures perfect adaptation, where steady-state output is independent of steady-state input. Here we quantitatively measure single-cell dynamics in the Saccharomyces cerevisiae hyperosmotic shock network, which regulates membrane turgor pressure. Importantly, we find that the nuclear enrichment of the MAP kinase Hog1 perfectly adapts to changes in external osmolarity, a feature robust to signaling fidelity and operating with very low noise. By monitoring multiple system quantities (e.g., cell volume, Hog1, glycerol) and using varied input waveforms (e.g., steps and ramps), we assess in a minimally invasive manner the network location of the mechanism responsible for perfect adaptation. We conclude that the system contains only one effective integrating mechanism, which requires Hog1 kinase activity and regulates glycerol synthesis but not leakage.National Science Foundation (U.S.) (Graduate Research Fellowship)Massachusetts Institute of Technology (MIT-Merck Graduate Fellowship)National Institutes of Health (U.S.) (NIH grant R01-GM068957)National Institutes of Health (U.S.) (NIH grant 5 R90 DK071511-01

Deconvolution of dynamic mechanical networks

Time-resolved single-molecule biophysical experiments yield data that contain a wealth of dynamic information, in addition to the equilibrium distributions derived from histograms of the time series. In typical force spectroscopic setups the molecule is connected via linkers to a read-out device, forming a mechanically coupled dynamic network. Deconvolution of equilibrium distributions, filtering out the influence of the linkers, is a straightforward and common practice. We have developed an analogous dynamic deconvolution theory for the more challenging task of extracting kinetic properties of individual components in networks of arbitrary complexity and topology. Our method determines the intrinsic linear response functions of a given molecule in the network, describing the power spectrum of conformational fluctuations. The practicality of our approach is demonstrated for the particular case of a protein linked via DNA handles to two optically trapped beads at constant stretching force, which we mimic through Brownian dynamics simulations. Each well in the protein free energy landscape (corresponding to folded, unfolded, or possibly intermediate states) will have its own characteristic equilibrium fluctuations. The associated linear response function is rich in physical content, since it depends both on the shape of the well and its diffusivity---a measure of the internal friction arising from such processes like the transient breaking and reformation of bonds in the protein structure. Starting from the autocorrelation functions of the equilibrium bead fluctuations measured in this force clamp setup, we show how an experimentalist can accurately extract the state-dependent protein diffusivity using a straightforward two-step procedure.Comment: 9 pages, 3 figures + supplementary material 14 pages, 4 figure

Chemotactic response and adaptation dynamics in Escherichia coli

Adaptation of the chemotaxis sensory pathway of the bacterium Escherichia coli is integral for detecting chemicals over a wide range of background concentrations, ultimately allowing cells to swim towards sources of attractant and away from repellents. Its biochemical mechanism based on methylation and demethylation of chemoreceptors has long been known. Despite the importance of adaptation for cell memory and behavior, the dynamics of adaptation are difficult to reconcile with current models of precise adaptation. Here, we follow time courses of signaling in response to concentration step changes of attractant using in vivo fluorescence resonance energy transfer measurements. Specifically, we use a condensed representation of adaptation time courses for efficient evaluation of different adaptation models. To quantitatively explain the data, we finally develop a dynamic model for signaling and adaptation based on the attractant flow in the experiment, signaling by cooperative receptor complexes, and multiple layers of feedback regulation for adaptation. We experimentally confirm the predicted effects of changing the enzyme-expression level and bypassing the negative feedback for demethylation. Our data analysis suggests significant imprecision in adaptation for large additions. Furthermore, our model predicts highly regulated, ultrafast adaptation in response to removal of attractant, which may be useful for fast reorientation of the cell and noise reduction in adaptation.Comment: accepted for publication in PLoS Computational Biology; manuscript (19 pages, 5 figures) and supplementary information; added additional clarification on alternative adaptation models in supplementary informatio

Robust Signal Processing in Living Cells

Cellular signaling networks have evolved an astonishing ability to function reliably and with high fidelity in uncertain environments. A crucial prerequisite for the high precision exhibited by many signaling circuits is their ability to keep the concentrations of active signaling compounds within tightly defined bounds, despite strong stochastic fluctuations in copy numbers and other detrimental influences. Based on a simple mathematical formalism, we identify topological organizing principles that facilitate such robust control of intracellular concentrations in the face of multifarious perturbations. Our framework allows us to judge whether a multiple-input-multiple-output reaction network is robust against large perturbations of network parameters and enables the predictive design of perfectly robust synthetic network architectures. Utilizing the Escherichia coli chemotaxis pathway as a hallmark example, we provide experimental evidence that our framework indeed allows us to unravel the topological organization of robust signaling. We demonstrate that the specific organization of the pathway allows the system to maintain global concentration robustness of the diffusible response regulator CheY with respect to several dominant perturbations. Our framework provides a counterpoint to the hypothesis that cellular function relies on an extensive machinery to fine-tune or control intracellular parameters. Rather, we suggest that for a large class of perturbations, there exists an appropriate topology that renders the network output invariant to the respective perturbations

Noise Filtering Strategies of Adaptive Signaling Networks: The Case of E. Coli Chemotaxis

Two distinct mechanisms for filtering noise in an input signal are identified in a class of adaptive sensory networks. We find that the high frequency noise is filtered by the output degradation process through time-averaging; while the low frequency noise is damped by adaptation through negative feedback. Both filtering processes themselves introduce intrinsic noises, which are found to be unfiltered and can thus amount to a significant internal noise floor even without signaling. These results are applied to E. coli chemotaxis. We show unambiguously that the molecular mechanism for the Berg-Purcell time-averaging scheme is the dephosphorylation of the response regulator CheY-P, not the receptor adaptation process as previously suggested. The high frequency noise due to the stochastic ligand binding-unbinding events and the random ligand molecule diffusion is averaged by the CheY-P dephosphorylation process to a negligible level in E.coli. We identify a previously unstudied noise source caused by the random motion of the cell in a ligand gradient. We show that this random walk induced signal noise has a divergent low frequency component, which is only rendered finite by the receptor adaptation process. For gradients within the E. coli sensing range, this dominant external noise can be comparable to the significant intrinsic noise in the system. The dependence of the response and its fluctuations on the key time scales of the system are studied systematically. We show that the chemotaxis pathway may have evolved to optimize gradient sensing, strong response, and noise control in different time scalesComment: 15 pages, 4 figure

Automated Ensemble Modeling with modelMaGe: Analyzing Feedback Mechanisms in the Sho1 Branch of the HOG Pathway

In systems biology uncertainty about biological processes translates into alternative mathematical model candidates. Here, the goal is to generate, fit and discriminate several candidate models that represent different hypotheses for feedback mechanisms responsible for downregulating the response of the Sho1 branch of the yeast high osmolarity glycerol (HOG) signaling pathway after initial stimulation. Implementing and testing these candidate models by hand is a tedious and error-prone task. Therefore, we automatically generated a set of candidate models of the Sho1 branch with the tool modelMaGe. These candidate models are automatically documented, can readily be simulated and fitted automatically to data. A ranking of the models with respect to parsimonious data representation is provided, enabling discrimination between candidate models and the biological hypotheses underlying them. We conclude that a previously published model fitted spurious effects in the data. Moreover, the discrimination analysis suggests that the reported data does not support the conclusion that a desensitization mechanism leads to the rapid attenuation of Hog1 signaling in the Sho1 branch of the HOG pathway. The data rather supports a model where an integrator feedback shuts down the pathway. This conclusion is also supported by dedicated experiments that can exclusively be predicted by those models including an integrator feedback