Empathy and parenthood: The moderating role of maternal trait empathy on parental burnout.

The ability to empathize with others enables us to effectively interact with each other and may have specifically evolved to support parental roles and caregiving. The relationship between parenting and trait empathy is little understood as previous research focused on empathy exclusively in the context of parenting, for example parental sensitivity. Here we aimed to understand how trait empathy may moderate the association between child's negative emotionality and parental burnout. Two cohorts were examined (1) parents of infants (10-18 months old; N = 203) and (2) parents of children (3-10 years old, N = 201). Parents filled out a battery of online questionnaires assessing maternal empathy, parental burnout and child temperament. We found that the relationship between higher levels of negative emotionality and parental burnout is moderated by specific aspects of maternal emotional empathy. Our findings suggest that maternal emotional empathy acts as a buffer against parental burnout when faced with a child's characteristics that incur higher parental demands.

Saccharomyces cerevisiae as a Toolkit for COP9 Signalosome Research.

The COP9 signalosome (CSN) is a highly conserved eukaryotic multi-subunit enzyme, regulating cullin RING ligase activities and accordingly, substrate ubiquitination and degradation. We showed that the CSN complex of Saccharomyces cerevisiae that is deviated in subunit composition and in sequence homology harbors a highly conserved cullin deneddylase enzymatic core complex. We took advantage of the non-essentiality of the S. cerevisiae CSN-NEDD8/Rub1 axis, together with the enzyme-substrate cross-species activity, to develop a sensitive fluorescence readout assay, suitable for biochemical assessment of cullin deneddylation by CSNs from various origins. We also demonstrated that the yeast catalytic subunit, CSN5/Jab1, is targeted by an inhibitor that was selected for the human orthologue. Treatment of yeast by the inhibitor led to the accumulation of neddylated cullins and the formation of reactive oxygen species. Overall, our data revealed S. cerevisiae as a general platform that can be used for studies of CSN deneddylation and for testing the efficacy of selected CSN inhibitors.

The COP9 signalosome mediates the Spt23 regulated fatty acid desaturation and ergosterol biosynthesis.

The COP9 signalosome (CSN) is a conserved eukaryotic complex, essential for vitality in all multicellular organisms and critical for the turnover of key cellular proteins through catalytic and non-catalytic activities. Saccharomyces cerevisiae is a powerful model organism for studying fundamental aspects of the CSN complex, since it includes a conserved enzymatic core but lacks non-catalytic activities, probably explaining its non-essentiality for life. A previous transcriptomic analysis of an S. cerevisiae strain deleted in the CSN5/RRI1 gene, encoding to the CSN catalytic subunit, revealed a downregulation of genes involved in lipid metabolism. We now show that the S. cerevisiae CSN holocomplex is essential for cellular lipid homeostasis. Defects in CSN assembly or activity lead to decreased quantities of ergosterol and unsaturated fatty acids (UFA); vacuole defects; diminished lipid droplets (LDs) size; and to accumulation of endoplasmic reticulum (ER) stress. The molecular mechanism behind these findings depends on CSN involvement in upregulating mRNA expression of SPT23. Spt23 is a novel activator of lipid desaturation and ergosterol biosynthesis. Our data reveal for the first time a functional link between the CSN holocomplex and Spt23. Moreover, CSN-dependent upregulation of SPT23 transcription is necessary for the fine-tuning of lipid homeostasis and for cellular health.

RGS6 and RGS7 Discriminate between the Highly Similar Gαi and Gαo Proteins Using a Two-Tiered Specificity Strategy.

RGS6 and RGS7 are regulators of G protein signaling (RGS) proteins that inactivate heterotrimeric (αßγ) G proteins and mediate diverse biological functions, such as cardiac and neuronal signaling. Uniquely, both RGS6 and RGS7 can discriminate between Gαo and Gαi1-two similar Gα subunits that belong to the same Gi sub-family. Here, we show that the isolated RGS domains of RGS6 and RGS7 are sufficient to achieve this specificity. We identified three specific RGS6/7 "disruptor residues" that can attenuate RGS interactions toward Gα subunits and demonstrated that their insertion into a representative high-activity RGS causes a significant, yet non-specific, reduction in activity. We further identified a unique "modulatory" residue that bypasses this negative effect, specifically toward Gαo. Hence, the exquisite specificity of RGS6 and RGS7 toward closely related Gα subunits is achieved via a two-tier specificity system, whereby a Gα-specific modulatory motif overrides the inhibitory effect of non-specific disruptor residues. Our findings expand the understanding of the molecular toolkit used by the RGS family to achieve specific interactions with selected Gα subunits-emphasizing the functional importance of the RGS domain in determining the activity and selectivity of RGS R7 sub-family members toward particular Gα subunits.

Sequential Independent Component Analysis Density Estimation.

A problem of multivariate probability density function estimation by exploiting linear independent components analysis (ICA) is addressed. Historically, ICA density estimation was initially proposed under the name projection pursuit density estimation (PPDE) and two basic methods, named forward and backward, were published. We derive a modification of the forward PPDE method, which avoids a computationally demanding optimization involving Monte Carlo sampling of the original method. The results of the experiments show that the proposed method presents an attractive choice for density estimation, which is pronounced for a small number of training observations. Under such conditions, our method usually outperforms model-based Gaussian mixture model. We also found that our method obtained better results than the backward PPDE methods in the situation of nonfactorizable underlying density functions. The proposed method has demonstrated a competitive performance compared with the support vector machine and the extreme learning machine in some real classification tasks.