Exploring a structural protein-drug interactome for new therapeutics in lung cancer

The pharmacology of drugs is often defined by more than one protein target. This property can be exploited to use approved drugs to uncover new targets and signaling pathways in cancer. Towards enabling a rational approach to uncover new targets, we expand a structural protein-ligand interactome () by scoring the interaction among 1000 FDA-approved drugs docked to 2500 pockets on protein structures of the human genome. This afforded a drug-target network whose properties compared favorably with previous networks constructed using experimental data. Among drugs with the highest degree and betweenness two are cancer drugs and one is currently used for treatment of lung cancer. Comparison of predicted cancer and non-cancer targets reveals that the most cancer-specific compounds were also the most selective compounds. Analysis of compound flexibility, hydrophobicity, and size showed that the most selective compounds were low molecular weight fragment-like heterocycles. We use a previously-developed screening approach using the cancer drug erlotinib as a template to screen other approved drugs that mimic its properties. Among the top 12 ranking candidates, four are cancer drugs, two of them kinase inhibitors (like erlotinib). Cellular studies using non-small cell lung cancer (NSCLC) cells revealed that several drugs inhibited lung cancer cell proliferation. We mined patient records at the Regenstrief Medical Record System to explore the possible association of exposure to three of these drugs with occurrence of lung cancer. Preliminary in vivo studies using the non-small cell lung cancer (NCLSC) xenograft model showed that losartan- and astemizole-treated mice had tumors that weighed 50 (p < 0.01) and 15 (p < 0.01) percent less than the treated controls. These results set the stage for further exploration of these drugs and to uncover new drugs for lung cancer therapy

Preparation of Material for Adsorption Ag(I) in the Solution

The application of silver in electronics, jewelry, catalytic and other industries often produces a large amount of silver-containing wastewater, which causes serious impact to the surrounding environment and human health, while silver has a certain economic value attached to it. Therefore, how to effectively treat and recover Ag(?) from the silver-containing wastewater is a hot topic of concern at present. In order to seek an efficient and environmentally friendly adsorbent, this paper compared the adsorption efficiency of purified, thermally modified, acid modified and thermally-acid modified Bentonite on silver, selected an economical and reasonable purified clay as a carrier, and then completed the preparation of modified Bentonite as well as the optimization of conditions with sodium silicate as a surfactant and 3-mercaptopropyltrimethoxysilane as a modifier. The experiments showed that under the conditions of sodium silicate dosage of 15% of Bentonite, Bentonite and modifier dosage of 1:1, solution pH of 9, temperature of 45 °C and modification time of 5 h, the synthesized sulfhydryl modified Bentonite has good adsorption performance on Ag(?), and its adsorption capacity can reach 293.7 mg·g-1

Oral Pathobiont Activates Anti-Apoptotic Pathway, Promoting both Immune Suppression and Oncogenic Cell Proliferation.

Chronic periodontitis (CP) is a microbial dysbiotic disease linked to increased risk of oral squamous cell carcinomas (OSCCs). To address the underlying mechanisms, mouse and human cell infection models and human biopsy samples were employed. We show that the \u27keystone\u27 pathogen Porphyromonas gingivalis, disrupts immune surveillance by generating myeloid-derived dendritic suppressor cells (MDDSCs) from monocytes. MDDSCs inhibit CTLs and induce FOXP3 + 

Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems

Funding: The work of Rachel W.-Y. Wu is funded through ETH grant ETH-05 19-1. Support from the Swiss National Science Foundation through projects PP00P2_170523 and PP00P2_198896 to Daniela I. V. Domeisen is gratefully acknowledged. Chaim I. Garfinkel and Chen Schwartz are supported by the ISF–NSFC joint research program (grant no. 3259/19). The work of Marisol Osman was supported by UBACyT20020170100428BA and PICT-2018-03046 projects. The work of Alvaro de la Cámara is funded by the Spanish Ministry of Science and Innovation through project PID2019-109107GB-I00. Blanca Ayarzagüena and Natalia Calvo acknowledge the support of the Spanish Ministry of Science and Innovation through the JeDiS (RTI2018-096402-B-I00) project. Froila M. Palmeiro and Javier García-Serrano have been partially supported by the Spanish ATLANTE project (PID2019-110234RB-C21) and Ramón y Cajal program (RYC-2016-21181), respectively. Neil P. Hindley and Corwin J. Wright are supported by UK Natural Environment Research Council (NERC), grant number NE/S00985X/1. Corwin J. Wright is also supported by a Royal Society University Research Fellowship UF160545. Seok-Woo Son and Hera Kim are supported by the Basic Science Research Program through the National Research Foundation of Korea (2017R1E1A1A01074889). his material is based upon work supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research (BER), Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling program under award no. DE-SC0022070 and National Science Foundation (NSF) IA 1947282. This work was also supported by the National Center for Atmospheric Research (NCAR), which is a major facility sponsored by the NSF under cooperative agreement no. 1852977. Pu Lin is supported by award NA18OAR4320123 from the National Oceanic and Atmospheric Administration (NOAA), U.S. Department of Commerce. Zachary D. Lawrence was partially supported under NOAA award NA20NWS4680051; Zachary D. Lawrence and Judith Perlwitz also acknowledge support from US federally appropriated funds.The stratosphere can be a source of predictability for surface weather on timescales of several weeks to months. However, the potential predictive skill gained from stratospheric variability can be limited by biases in the representation of stratospheric processes and the coupling of the stratosphere with surface climate in forecast systems. This study provides a first systematic identification of model biases in the stratosphere across a wide range of subseasonal forecast systems. It is found that many of the forecast systems considered exhibit warm global-mean temperature biases from the lower to middle stratosphere, too strong/cold wintertime polar vortices, and too cold extratropical upper-troposphere/lower-stratosphere regions. Furthermore, tropical stratospheric anomalies associated with the Quasi-Biennial Oscillation tend to decay toward each system's climatology with lead time. In the Northern Hemisphere (NH), most systems do not capture the seasonal cycle of extreme-vortex-event probabilities, with an underestimation of sudden stratospheric warming events and an overestimation of strong vortex events in January. In the Southern Hemisphere (SH), springtime interannual variability in the polar vortex is generally underestimated, but the timing of the final breakdown of the polar vortex often happens too early in many of the prediction systems. These stratospheric biases tend to be considerably worse in systems with lower model lid heights. In both hemispheres, most systems with low-top atmospheric models also consistently underestimate the upward wave driving that affects the strength of the stratospheric polar vortex. We expect that the biases identified here will help guide model development for subseasonal-to-seasonal forecast systems and further our understanding of the role of the stratosphere in predictive skill in the troposphere.Peer reviewe

Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems

The stratosphere can be a source of predictability for surface weather on timescales of several weeks to months. However, the potential predictive skill gained from stratospheric variability can be limited by biases in the representation of stratospheric processes and the coupling of the stratosphere with surface climate in forecast systems. This study provides a first systematic identification of model biases in the stratosphere across a wide range of subseasonal forecast systems. It is found that many of the forecast systems considered exhibit warm global-mean temperature biases from the lower to middle stratosphere, too strong/cold wintertime polar vortices, and too cold extratropical upper-troposphere/lower-stratosphere regions. Furthermore, tropical stratospheric anomalies associated with the Quasi-Biennial Oscillation tend to decay toward each system\u27s climatology with lead time. In the Northern Hemisphere (NH), most systems do not capture the seasonal cycle of extreme-vortex-event probabilities, with an underestimation of sudden stratospheric warming events and an overestimation of strong vortex events in January. In the Southern Hemisphere (SH), springtime interannual variability in the polar vortex is generally underestimated, but the timing of the final breakdown of the polar vortex often happens too early in many of the prediction systems. These stratospheric biases tend to be considerably worse in systems with lower model lid heights. In both hemispheres, most systems with low-top atmospheric models also consistently underestimate the upward wave driving that affects the strength of the stratospheric polar vortex. We expect that the biases identified here will help guide model development for subseasonal-to-seasonal forecast systems and further our understanding of the role of the stratosphere in predictive skill in the troposphere

Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems

The stratosphere can be a source of predictability for surface weather on timescales of several weeks to months. However, the potential predictive skill gained from stratospheric variability can be limited by biases in the representation of stratospheric processes and the coupling of the stratosphere with surface climate in forecast systems. This study provides a first systematic identification of model biases in the stratosphere across a wide range of subseasonal forecast systems. It is found that many of the forecast systems considered exhibit warm global-mean temperature biases from the lower to middle stratosphere, too strong/cold wintertime polar vortices, and too cold extratropical upper-troposphere/lower-stratosphere regions. Furthermore, tropical stratospheric anomalies associated with the Quasi-Biennial Oscillation tend to decay toward each system's climatology with lead time. In the Northern Hemisphere (NH), most systems do not capture the seasonal cycle of extreme-vortex-event probabilities, with an underestimation of sudden stratospheric warming events and an overestimation of strong vortex events in January. In the Southern Hemisphere (SH), springtime interannual variability in the polar vortex is generally underestimated, but the timing of the final breakdown of the polar vortex often happens too early in many of the prediction systems. These stratospheric biases tend to be considerably worse in systems with lower model lid heights. In both hemispheres, most systems with low-top atmospheric models also consistently underestimate the upward wave driving that affects the strength of the stratospheric polar vortex. We expect that the biases identified here will help guide model development for subseasonal-to-seasonal forecast systems and further our understanding of the role of the stratosphere in predictive skill in the troposphere