A glimpse of the ERM proteins

In all eukaryotes, the plasma membrane is critically important as it maintains the architectural integrity of the cell. Proper anchorage and interaction between the plasma membrane and the cytoskeleton is critical for normal cellular processes. The ERM (ezrin-radixin-moesin) proteins are a class of highly homologous proteins involved in linking the plasma membrane to the cortical actin cytoskeleton. This review takes a succinct look at the biology of the ERM proteins including their structure and function. Current reports on their regulation that leads to activation and deactivation was examined before taking a look at the different interacting partners. Finally, emerging roles of each of the ERM family members in cancer was highlighted

The relationship of employment to self-perception and well-being in women: A cognitive analysis

We investigated the relationship of employment to self-perception and well-being in women who held two different cognitive orientations toward paid work (career oriented and not career oriented) and who also varied in the degree to which their current employment status realized their view of paid work. The data were taken from a 1977 survey of a large sample of women and included responses to questions about self-esteem, life satisfaction, and self-perception. The results indicated that career-oriented women who were employed full time were happier with themselves and their lives than those employed part time or not employed. In contrast, for women who were not career oriented, being employed was not associated with greater self-esteem or well-being. In addition, career-oriented women who were employed were the most likely to emphasize job-related characteristics in their self-descriptions. These findings suggest that the psychological benefits of employment that have been identified repeatedly in previous research are likely to be accrued only by women who regard paid work as a meaningful self-fulfilling activity.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45581/1/11199_2004_Article_BF00288148.pd

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships

Overexpression of LASP-1 mediates migration and proliferation of human ovarian cancer cells and influences zyxin localisation

LIM and SH3 protein 1 (LASP-1), initially identified from human breast cancer, is a specific focal adhesion protein involved in cell proliferation and migration. In the present work, we analysed the effect of LASP-1 on biology and function of human ovarian cancer cell line SKOV-3 using small interfering RNA technique (siRNA).Transfection with LASP-1-specific siRNA resulted in a reduced protein level of LASP-1 in SKOV-3 cells. The siRNA-treated cells were arrested in G2/M phase of the cell cycle and proliferation of the tumour cells was suppressed by 60–90% corresponding to around 70% of the cells being transfected successfully as seen by immunofluorescence. Moreover, transfected tumour cells showed a 40% reduced migration. LASP-1 silencing is accompanied by a reduced binding of the LASP-1-binding partner zyxin to focal contacts without changes in actin stress fibre and microtubule organisation or focal adhesion morphology as observed by immunofluorescence. In contrast, silencing of zyxin is not influencing cell migration and had neither influence on LASP-1 expression nor actin cytoskeleton and focal contact morphology suggesting that LASP-1 is necessary and sufficient for recruiting zyxin to focal contacts.The data provide evidence for an essential role of LASP-1 in tumour cell growth and migration, possibly through influencing zyxin localization

The mammalian centrosome and its functional significance

Primarily known for its role as major microtubule organizing center, the centrosome is increasingly being recognized for its functional significance in key cell cycle regulating events. We are now at the beginning of understanding the centrosome’s functional complexities and its major impact on directing complex interactions and signal transduction cascades important for cell cycle regulation. The centrosome orchestrates entry into mitosis, anaphase onset, cytokinesis, G1/S transition, and monitors DNA damage. Recently, the centrosome has also been recognized as major docking station where regulatory complexes accumulate including kinases and phosphatases as well as numerous other cell cycle regulators that utilize the centrosome as platform to coordinate multiple cell cycle-specific functions. Vesicles that are translocated along microtubules to and away from centrosomes may also carry enzymes or substrates that use centrosomes as main docking station. The centrosome’s role in various diseases has been recognized and a wealth of data has been accumulated linking dysfunctional centrosomes to cancer, Alstrom syndrome, various neurological disorders, and others. Centrosome abnormalities and dysfunctions have been associated with several types of infertility. The present review highlights the centrosome’s significant roles in cell cycle events in somatic and reproductive cells and discusses centrosome abnormalities and implications in disease

Revising working models across time: Relationship situations that enhance attachment security

We propose the Attachment Security Enhancement Model (ASEM) to suggest how romantic relationships can promote chronic attachment security. One part of the ASEM examines partner responses that protect relationships from the erosive effects of immediate insecurity, but such responses may not necessarily address underlying insecurities in a person’s mental models. Therefore, a second part of the ASEM examines relationship situations that foster more secure mental models. Both parts may work in tandem. We posit that attachment anxiety should decline most in situations that foster greater personal confidence and more secure mental models of the self. In contrast, attachment avoidance should decline most in situations that involve positive dependence and foster more secure models of close others. The ASEM integrates research and theory, suggests novel directions for future research, and has practical implications, all of which center on the idea that adult attachment orientations are an emergent property of close relationships

Dynamic relocalization of NHERF1 mediates chemotactic migration of ovarian cancer cells toward lysophosphatidic acid stimulation

NHERF1/EBP50 (Na+/H+ exchanger regulating factor 1; Ezrin-binding phosphoprotein of 50 kDa) organizes stable protein complexes beneath the apical membrane of polar epithelial cells. By contrast, in cancer cells without any fixed polarity, NHERF1 often localizes in the cytoplasm. The regulation of cytoplasmic NHERF1 and its role in cancer progression remain unclear. In this study, we found that, upon lysophosphatidic acid (LPA) stimulation, cytoplasmic NHERF1 rapidly translocated to the plasma membrane, and subsequently to cortical protrusion structures, of ovarian cancer cells. This movement depended on direct binding of NHERF1 to C-terminally phosphorylated ERM proteins (cpERMs). Moreover, NHERF1 depletion downregulated cpERMs and further impaired cpERM-dependent remodeling of the cell cortex, suggesting reciprocal regulation between these proteins. The LPA-induced protein complex was highly enriched in migratory pseudopodia, whose formation was impaired by overexpression of NHERF1 truncation mutants. Consistent with this, NHERF1 depletion in various types of cancer cells abolished chemotactic cell migration toward a LPA gradient. Taken together, our findings suggest that the high dynamics of cytosolic NHERF1 provide cancer cells with a means of controlling chemotactic migration. This capacity is likely to be essential for ovarian cancer progression in tumor microenvironments containing LPA

Lectures on the Calabi-Yau Landscape

In these lecture notes, we survey the landscape of Calabi-Yau threefolds, and the use of machine learning to explore it. We begin with the compact portion of the landscape, focusing in particular on complete intersection Calabi-Yau varieties (CICYs) and elliptic fibrations. Non-compact Calabi-Yau manifolds are manifest in Type II superstring theories, they arise as representation varieties of quivers, used to describe gauge theories in the bulk familiar four dimensions. Finally, given the huge amount of Calabi-Yau data, whether and how machine learning can be applied to algebraic geometry and string landscape is also discussed. These notes are directed to the beginning graduate student interested in mathematics and in physics, and are based on lectures given by the 2nd author at the 2019 PIMS Summer School on Algebraic Geometry in High-Energy Physics at the University of Saskatchewan