Genetic and environmental influences on sleep quality in middle‐aged men: a twin study

Poor sleep quality is a risk factor for a number of cognitive and physiological age-related disorders. Identifying factors underlying sleep quality are important in understanding the etiology of these age-related health disorders. We investigated the extent to which genes and the environment contribute to subjective sleep quality in middle-aged male twins using the classical twin design. We used the Pittsburgh Sleep Quality Index to measure sleep quality in 1218 middle-aged twin men from the Vietnam Era Twin Study of Aging (mean age = 55.4 years; range 51-60; 339 monozygotic twin pairs, 257 dizygotic twin pairs, 26 unpaired twins). The mean PSQI global score was 5.6 [SD = 3.6; range 0-20]. Based on univariate twin models, 34% of variability in the global PSQI score was due to additive genetic effects (heritability) and 66% was attributed to individual-specific environmental factors. Common environment did not contribute to the variability. Similarly, the heritability of poor sleep-a dichotomous measure based on the cut-off of global PSQI>5-was 31%, with no contribution of the common environment. Heritability of six of the seven PSQI component scores (subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, and daytime dysfunction) ranged from 0.15 to 0.31, whereas no genetic influences contributed to the use of sleeping medication. Additive genetic influences contribute to approximately one-third of the variability of global subjective sleep quality. Our results in middle-aged men constitute a first step towards examination of the genetic relationship between sleep and other facets of aging.Accepted manuscrip

Spin-polarized imaging of strongly interacting fermions in the ferrimagnetic state of Weyl candidate CeBi

CeBi has an intricate magnetic phase diagram whose fully-polarized state has recently been suggested as a Weyl semimetal, though the role of $f$ states in promoting strong interactions has remained elusive. Here we focus on the less-studied, but also time-reversal symmetry-breaking ferrimagnetic phase of CeBi, where our density functional theory (DFT) calculations predict additional Weyl nodes near the Fermi level $E_\mathrm{F}$. We use spin-polarized scanning tunneling microscopy and spectroscopy to image the surface ferrimagnetic order on the itinerant Bi $p$ states, indicating their orbital hybridization with localized Ce $f$ states. We observe suppression of this spin-polarized signature at $E_\mathrm{F}$, coincident with a Fano line shape in the conductance spectra, suggesting the Bi $p$ states partially Kondo screen the $f$ magnetic moments, and this $p-f$ hybridization causes strong Fermi-level band renormalization. The $p$ band flattening is supported by our quasiparticle interference (QPI) measurements, which also show band splitting in agreement with DFT, painting a consistent picture of a strongly interacting magnetic Weyl semimetal

IL-2 production correlates with effector cell differentiation in HIV-specific CD8+ T cells

BACKGROUND: Diminished IL-2 production and lack of effector differentiation have been reported for HIV-specific T cells. In this study, we examined the prevalence of these phenomena using 8-color cytokine flow cytometry, and tested the hypothesis that these two findings were causally related. We analyzed cytokine profiles and memory/effector phenotypes of HIV-specific and CMV-specific T cells using short-term in vitro stimulation with HIV or CMV peptide pools. Nineteen HIV-positive subjects with progressive disease and twenty healthy, HIV-negative subjects were examined. RESULTS: Among HIV-infected subjects, there were significantly fewer CD8+ IL-2+ T cells responding to HIV compared to CMV, with no significant difference in CD4+ IL-2+ T cells. The majority of CMV-specific T cells in both HIV-negative and HIV-positive subjects appeared to be terminally differentiated effector cells (CD8+ CD27- CD28- CD45RA+ or CD8+ CD27- CD28- CD45RA-). In HIV-positive subjects, the most common phenotype of HIV-specific T cells was intermediate in differentiation (CD8+ CD27+ CD28- CD45RA-). These differences were statistically significant, both as absolute cell frequencies and as percentages. There was a significant correlation between the absolute number of HIV-specific CD8+ IL-2+ T cells and HIV-specific CD8+ CD27- CD28- CD45RA+ terminal effector cells. CONCLUSION: IL-2 production from antigen-specific CD8+ T cells correlates with effector cell differentiation of those cells

Child Under-weight and Agricultural Productivity in India: Implications for Public Provisioning and Women’s Agency

This study is part of the ongoing research program on Leveraging Agriculture for Nutrition in South Asia (LANSA) funded by UK Aid from the Department for International Development, UK. The authors are consultants or regular staff of M.S. Swaminathan Research Foundation, India, one of the six partner institutions of LANSA.A recent global hunger index indicated a 12 percent decline in child underweight rates. This study attempts an empirical explanation of the factors that influence child underweight rates at the district level. Agricultural land productivity, share of women educated above the secondary level and participating in work, maternal, and child health seem to contribute to the reduction in child underweight. However government health and water supply facilities turn out to be ineffective

RNA Sequencing of Pancreatic Circulating Tumour Cells Implicates WNT Signaling in Metastasis

Circulating tumour cells (CTCs) shed into blood from primary cancers include putative precursors that initiate distal metastases. While these cells are extraordinarily rare, they may identify cellular pathways contributing to the blood-borne dissemination of cancer. Here, we adapted a microfluidic device for efficient capture of CTCs from an endogenous mouse pancreatic cancer model and subjected CTCs to single molecule RNA sequencing, identifying Wnt2 as a candidate gene enriched in CTCs. Expression of Wnt2 in pancreatic cancer cells suppresses anoikis, enhances anchorage-independent sphere formation, and increases metastatic propensity in vivo. This effect is correlated with fibronectin upregulation and suppressed by inhibition of Map3k7 (Tak1) kinase. In humans, formation of non-adherent tumour spheres by pancreatic cancer cells is associated with upregulation of multiple Wnt genes, and pancreatic CTCs revealed enrichment for Wnt signaling in 5 of 11 cases. Thus, molecular analysis of CTCs may identify candidate therapeutic targets to prevent the distal spread of cancer

Computational Prediction of Heme-Binding Residues by Exploiting Residue Interaction Network

Computational identification of heme-binding residues is beneficial for predicting and designing novel heme proteins. Here we proposed a novel method for heme-binding residue prediction by exploiting topological properties of these residues in the residue interaction networks derived from three-dimensional structures. Comprehensive analysis showed that key residues located in heme-binding regions are generally associated with the nodes with higher degree, closeness and betweenness, but lower clustering coefficient in the network. HemeNet, a support vector machine (SVM) based predictor, was developed to identify heme-binding residues by combining topological features with existing sequence and structural features. The results showed that incorporation of network-based features significantly improved the prediction performance. We also compared the residue interaction networks of heme proteins before and after heme binding and found that the topological features can well characterize the heme-binding sites of apo structures as well as those of holo structures, which led to reliable performance improvement as we applied HemeNet to predicting the binding residues of proteins in the heme-free state. HemeNet web server is freely accessible at http://mleg.cse.sc.edu/hemeNet/

IHMCIF: An Extension of the PDBx/mmCIF Data Standard for Integrative Structure Determination Methods

IHMCIF (github.com/ihmwg/IHMCIF) is a data information framework that supports archiving and disseminating macromolecular structures determined by integrative or hybrid modeling (IHM), and making them Findable, Accessible, Interoperable, and Reusable (FAIR). IHMCIF is an extension of the Protein Data Bank Exchange/macromolecular Crystallographic Information Framework (PDBx/mmCIF) that serves as the framework for the Protein Data Bank (PDB) to archive experimentally determined atomic structures of biological macromolecules and their complexes with one another and small molecule ligands (e.g., enzyme cofactors and drugs). IHMCIF serves as the foundational data standard for the PDB-Dev prototype system, developed for archiving and disseminating integrative structures. It utilizes a flexible data representation to describe integrative structures that span multiple spatiotemporal scales and structural states with definitions for restraints from a variety of experimental methods contributing to integrative structural biology. The IHMCIF extension was created with the benefit of considerable community input and recommendations gathered by the Worldwide Protein Data Bank (wwPDB) Task Force for Integrative or Hybrid Methods (wwpdb.org/task/hybrid). Herein, we describe the development of IHMCIF to support evolving methodologies and ongoing advancements in integrative structural biology. Ultimately, IHMCIF will facilitate the unification of PDB-Dev data and tools with the PDB archive so that integrative structures can be archived and disseminated through PDB

False positive reduction in protein-protein interaction predictions using gene ontology annotations

<p>Abstract</p> <p>Background</p> <p>Many crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated.</p> <p>Results</p> <p>Gene Ontology (GO) annotations were used to reduce false positive protein-protein interactions (PPI) pairs resulting from computational predictions. Using experimentally obtained PPI pairs as a training dataset, eight top-ranking keywords were extracted from GO molecular function annotations. The sensitivity of these keywords is 64.21% in the yeast experimental dataset and 80.83% in the worm experimental dataset. The specificities, a measure of recovery power, of these keywords applied to four predicted PPI datasets for each studied organisms, are 48.32% and 46.49% (by average of four datasets) in yeast and worm, respectively. Based on eight top-ranking keywords and co-localization of interacting proteins a set of two knowledge rules were deduced and applied to remove false positive protein pairs. The '<it>strength</it>', a measure of improvement provided by the rules was defined based on the signal-to-noise ratio and implemented to measure the applicability of knowledge rules applying to the predicted PPI datasets. Depending on the employed PPI-predicting methods, the <it>strength </it>varies between two and ten-fold of randomly removing protein pairs from the datasets.</p> <p>Conclusion</p> <p>Gene Ontology annotations along with the deduced knowledge rules could be implemented to partially remove false predicted PPI pairs. Removal of false positives from predicted datasets increases the true positive fractions of the datasets and improves the robustness of predicted pairs as compared to random protein pairing, and eventually results in better overlap with experimental results.</p

Oncogenic KRAS engages an RSK1/NF1 pathway to inhibit wild-type RAS signaling in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with limited treatment options. Although activating mutations of the KRAS GTPase are the predominant dependency present in >90% of PDAC patients, targeting KRAS mutants directly has been challenging in PDAC. Similarly, strategies targeting known KRAS downstream effectors have had limited clinical success due to feedback mechanisms, alternate pathways, and dose-limiting toxicities in normal tissues. Therefore, identifying additional functionally relevant KRAS interactions in PDAC may allow for a better understanding of feedback mechanisms and unveil potential therapeutic targets. Here, we used proximity labeling to identify protein interactors of active KRAS in PDAC cells. We expressed fusions of wild-type (WT) (BirA-KRAS4B), mutant (BirA-KRAS4BG12D), and nontransforming cytosolic double mutant (BirA-KRAS4BG12D/C185S) KRAS with the BirA biotin ligase in murine PDAC cells. Mass spectrometry analysis revealed that RSK1 selectively interacts with membrane-bound KRASG12D, and we demonstrate that this interaction requires NF1 and SPRED2. We find that membrane RSK1 mediates negative feedback on WT RAS signaling and impedes the proliferation of pancreatic cancer cells upon the ablation of mutant KRAS. Our findings link NF1 to the membrane-localized functions of RSK1 and highlight a role for WT RAS signaling in promoting adaptive resistance to mutant KRAS-specific inhibitors in PDAC

α-Synuclein impairs macroautophagy: implications for Parkinson’s disease

α-Synuclein impairs autophagosome formation and mislocalizes Atg9 by inhibiting Rab1a