Incidence of reversible amenorrhea in women with breast cancer undergoing adjuvant anthracycline-based chemotherapy with or without docetaxel

<p>Abstract</p> <p>Background</p> <p>To determine the incidence of reversible amenorrhea in women with breast cancer undergoing adjuvant anthracycline-based chemotherapy with or without docetaxel.</p> <p>Methods</p> <p>We studied the incidence and duration of amenorrhea induced by two chemotherapy regimens: (i) 6 cycles of 5-fluorouracil 500 mg/m², epirubicin 100 mg/m²and cyclophosphamide 500 mg/m²on day 1 every 3 weeks (6FEC) and (ii) 3 cycles of FEC 100 followed by 3 cycles of docetaxel 100 mg/m²on day 1 every 3 weeks (3FEC/3D). Reversible amenorrhea was defined as recovery of regular menses and, where available (101 patients), premenopausal hormone values (luteinizing hormone (LH), follicle-stimulating hormone (FSH) and estradiol) in the year following the end of chemotherapy.</p> <p>Results</p> <p>One hundred and fifty-four premenopausal patients were included: 84 treated with 6FEC and 70 with 3FEC/3D. The median age was 43.5 years (range: 28–58) in the 6FEC arm and 44 years (range: 29–53) in the 3FEC/3D arm. Seventy-eight percent of patients were treated in the context of the PACS 01 trial. The incidence of chemotherapy-induced amenorrhea at the end of chemotherapy was similar in the two groups: 93 % in the 6FEC arm and 92.8 % in the 3FEC/3D arm. However, in the year following the end of chemotherapy, more patients recovered menses in the 3FEC/3D arm than in the 6FEC arm: 35.5 % versus 23.7 % (p = 0.019). Among the 101 patients for whom hormone values were available, 43 % in the 3FEC/3D arm and 29 % in the 6FEC arm showed premenopausal levels one year after the end of chemotherapy (p < 0.01). In the 3FEC/3D group, there was a statistically significant advantage in disease-free survival (DFS) for patients who were still amenorrheic after one year, compared to patients who had recovered regular menses (p = 0.0017).</p> <p>Conclusion</p> <p>Our study suggests that 3FEC/3D treatment induces more reversible amenorrhea than 6FEC. The clinical relevance of these findings needs to be investigated further.</p

Isotocin controls ion regulation through regulating ionocyte progenitor differentiation and proliferation

The present study using zebrafish as a model explores the role of isotocin, a homolog of oxytocin, in controlling ion regulatory mechanisms. Double-deionized water treatment for 24 h significantly stimulated isotocin mRNA expression in zebrafish embryos. Whole-body Cl−, Ca2+, and Na+ contents, mRNA expressions of ion transporters and ionocyte-differentiation related transcription factors, and the number of skin ionocytes decreased in isotocin morphants. In contrast, overexpression of isotocin caused an increase in ionocyte numbers. Isotocin morpholino caused significant suppression of foxi3a mRNA expression, while isotocin cRNA stimulated foxi3a mRNA expressions at the tail-bud stage of zebrafish embryos. The density of P63 (an epidermal stem cell marker)-positive cells was downregulated by isotocin morpholinos and was upregulated by isotocin cRNA. Taken together, isotocin stimulates the proliferation of epidermal stem cells and differentiation of ionocyte progenitors by regulating the P63 and Foxi3a transcription factors, consequently enhancing the functional activities of ionocytes

Validation of Coevolving Residue Algorithms via Pipeline Sensitivity Analysis: ELSC and OMES and ZNMI, Oh My!

Correlated amino acid substitution algorithms attempt to discover groups of residues that co-fluctuate due to either structural or functional constraints. Although these algorithms could inform both ab initio protein folding calculations and evolutionary studies, their utility for these purposes has been hindered by a lack of confidence in their predictions due to hard to control sources of error. To complicate matters further, naive users are confronted with a multitude of methods to choose from, in addition to the mechanics of assembling and pruning a dataset. We first introduce a new pair scoring method, called ZNMI (Z-scored-product Normalized Mutual Information), which drastically improves the performance of mutual information for co-fluctuating residue prediction. Second and more important, we recast the process of finding coevolving residues in proteins as a data-processing pipeline inspired by the medical imaging literature. We construct an ensemble of alignment partitions that can be used in a cross-validation scheme to assess the effects of choices made during the procedure on the resulting predictions. This pipeline sensitivity study gives a measure of reproducibility (how similar are the predictions given perturbations to the pipeline?) and accuracy (are residue pairs with large couplings on average close in tertiary structure?). We choose a handful of published methods, along with ZNMI, and compare their reproducibility and accuracy on three diverse protein families. We find that (i) of the algorithms tested, while none appear to be both highly reproducible and accurate, ZNMI is one of the most accurate by far and (ii) while users should be wary of predictions drawn from a single alignment, considering an ensemble of sub-alignments can help to determine both highly accurate and reproducible couplings. Our cross-validation approach should be of interest both to developers and end users of algorithms that try to detect correlated amino acid substitutions

Using Sequence Similarity Networks for Visualization of Relationships Across Diverse Protein Superfamilies

The dramatic increase in heterogeneous types of biological data—in particular, the abundance of new protein sequences—requires fast and user-friendly methods for organizing this information in a way that enables functional inference. The most widely used strategy to link sequence or structure to function, homology-based function prediction, relies on the fundamental assumption that sequence or structural similarity implies functional similarity. New tools that extend this approach are still urgently needed to associate sequence data with biological information in ways that accommodate the real complexity of the problem, while being accessible to experimental as well as computational biologists. To address this, we have examined the application of sequence similarity networks for visualizing functional trends across protein superfamilies from the context of sequence similarity. Using three large groups of homologous proteins of varying types of structural and functional diversity—GPCRs and kinases from humans, and the crotonase superfamily of enzymes—we show that overlaying networks with orthogonal information is a powerful approach for observing functional themes and revealing outliers. In comparison to other primary methods, networks provide both a good representation of group-wise sequence similarity relationships and a strong visual and quantitative correlation with phylogenetic trees, while enabling analysis and visualization of much larger sets of sequences than trees or multiple sequence alignments can easily accommodate. We also define important limitations and caveats in the application of these networks. As a broadly accessible and effective tool for the exploration of protein superfamilies, sequence similarity networks show great potential for generating testable hypotheses about protein structure-function relationships

Predicting Novel Binding Modes of Agonists to β Adrenergic Receptors Using All-Atom Molecular Dynamics Simulations

Understanding the binding mode of agonists to adrenergic receptors is crucial to enabling improved rational design of new therapeutic agents. However, so far the high conformational flexibility of G protein-coupled receptors has been an obstacle to obtaining structural information on agonist binding at atomic resolution. In this study, we report microsecond classical molecular dynamics simulations of β1 and β2 adrenergic receptors bound to the full agonist isoprenaline and in their unliganded form. These simulations show a novel agonist binding mode that differs from the one found for antagonists in the crystal structures and from the docking poses reported by in silico docking studies performed on rigid receptors. Internal water molecules contribute to the stabilization of novel interactions between ligand and receptor, both at the interface of helices V and VI with the catechol group of isoprenaline as well as at the interface of helices III and VII with the ethanolamine moiety of the ligand. Despite the fact that the characteristic N-C-C-OH motif is identical in the co-crystallized ligands and in the full agonist isoprenaline, the interaction network between this group and the anchor site formed by Asp(3.32) and Asn(7.39) is substantially different between agonists and inverse agonists/antagonists due to two water molecules that enter the cavity and contribute to the stabilization of a novel network of interactions. These new binding poses, together with observed conformational changes in the extracellular loops, suggest possible determinants of receptor specificity

Comparison of Peptide Array Substrate Phosphorylation of c-Raf and Mitogen Activated Protein Kinase Kinase Kinase 8

Kinases are pivotal regulators of cellular physiology. The human genome contains more than 500 putative kinases, which exert their action via the phosphorylation of specific substrates. The determinants of this specificity are still only partly understood and as a consequence it is difficult to predict kinase substrate preferences from the primary structure, hampering the understanding of kinase function in physiology and prompting the development of technologies that allow easy assessment of kinase substrate consensus sequences. Hence, we decided to explore the usefulness of phosphorylation of peptide arrays comprising of 1176 different peptide substrates with recombinant kinases for determining kinase substrate preferences, based on the contribution of individual amino acids to total array phosphorylation. Employing this technology, we were able to determine the consensus peptide sequences for substrates of both c-Raf and Mitogen Activated Protein Kinase Kinase Kinase 8, two highly homologous kinases with distinct signalling roles in cellular physiology. The results show that although consensus sequences for these two kinases identified through our analysis share important chemical similarities, there is still some sequence specificity that could explain the different biological action of the two enzymes. Thus peptide arrays are a useful instrument for deducing substrate consensus sequences and highly homologous kinases can differ in their requirement for phosphorylation events

Systematic generation of in vivo G protein-coupled receptor mutants in the rat

G-protein-coupled receptors (GPCRs) constitute a large family of cell surface receptors that are involved in a wide range of physiological and pathological processes, and are targets for many therapeutic interventions. However, genetic models in the rat, one of the most widely used model organisms in physiological and pharmacological research, are largely lacking. Here, we applied N-ethyl-N-nitrosourea (ENU)-driven target-selected mutagenesis to generate an in vivo GPCR mutant collection in the rat. A pre-selected panel of 250 human GPCR homologs was screened for mutations in 813 rats, resulting in the identification of 131 non-synonymous mutations. From these, seven novel potential rat gene knockouts were established as well as 45 lines carrying missense mutations in various genes associated with or involved in human diseases. We provide extensive in silico modeling results of the missense mutations and show experimental data, suggesting loss-of-function phenotypes for several models, including Mc4r and Lpar1. Taken together, the approach used resulted not only in a set of novel gene knockouts, but also in allelic series of more subtle amino acid variants, similar as commonly observed in human disease. The mutants presented here may greatly benefit studies to understand specific GPCR function and support the development of novel therapeutic strategies

Molecular Basis of Ligand Dissociation in β-Adrenergic Receptors

The important and diverse biological functions of β-adrenergic receptors (βARs) have promoted the search for compounds to stimulate or inhibit their activity. In this regard, unraveling the molecular basis of ligand binding/unbinding events is essential to understand the pharmacological properties of these G protein-coupled receptors. In this study, we use the steered molecular dynamics simulation method to describe, in atomic detail, the unbinding process of two inverse agonists, which have been recently co-crystallized with β1 and β2ARs subtypes, along four different channels. Our results indicate that this type of compounds likely accesses the orthosteric binding site of βARs from the extracellular water environment. Importantly, reconstruction of forces and energies from the simulations of the dissociation process suggests, for the first time, the presence of secondary binding sites located in the extracellular loops 2 and 3 and transmembrane helix 7, where ligands are transiently retained by electrostatic and Van der Waals interactions. Comparison of the residues that form these new transient allosteric binding sites in both βARs subtypes reveals the importance of non-conserved electrostatic interactions as well as conserved aromatic contacts in the early steps of the binding process

Team climate, intention to leave and turnover among hospital employees: Prospective cohort study

<p>Abstract</p> <p>Background</p> <p>In hospitals, the costs of employee turnover are substantial and intentions to leave among staff may manifest as lowered performance. We examined whether team climate, as indicated by clear and shared goals, participation, task orientation and support for innovation, predicts intention to leave the job and actual turnover among hospital employees.</p> <p>Methods</p> <p>Prospective study with baseline and follow-up surveys (2–4 years apart). The participants were 6,441 (785 men, 5,656 women) hospital employees under the age of 55 at the time of follow-up survey. Logistic regression with generalized estimating equations was used as an analysis method to include both individual and work unit level predictors in the models.</p> <p>Results</p> <p>Among stayers with no intention to leave at baseline, lower self-reported team climate predicted higher likelihood of having intentions to leave at follow-up (odds ratio per 1 standard deviation decrease in team climate was 1.6, 95% confidence interval 1.4–1.8). Lower co-worker assessed team climate at follow-up was also association with such intentions (odds ratio 1.8, 95% confidence interval 1.4–2.4). Among all participants, the likelihood of actually quitting the job was higher for those with poor self-reported team climate at baseline. This association disappeared after adjustment for intention to leave at baseline suggesting that such intentions may explain the greater turnover rate among employees with low team climate.</p> <p>Conclusion</p> <p>Improving team climate may reduce intentions to leave and turnover among hospital employees.</p