Genetic and epigenetic defects in Wnt/beta-catenin signaling play important roles in colorectal cancer progression. Here we identify DACT3, a member of the DACT (Dpr/Frodo) gene family, as a negative regulator of Wnt/beta-catenin signaling that is transcriptionally repressed in colorectal cancer. Unlike other Wnt signaling inhibitors that are silenced by DNA methylation, DACT3 repression is associated with bivalent histone modifications. Remarkably, DACT3 expression can be robustly derepressed by a pharmacological combination that simultaneously targets both histone methylation and deacetylation, leading to strong inhibition of Dishevelled (Dvl)-mediated Wnt/beta-catenin signaling and massive apoptosis of colorectal cancer cells. Our study identifies DACT3 as an important regulator of Wnt/beta-catenin signaling in colorectal cancer and suggests a potential strategy for therapeutic control of Wnt/beta-catenin signaling in colorectal cancer.

Stimulation of insulin secretion by glucose and other secretagogues from pancreatic islet β-cells is mediated by multiple signaling pathways. Rac1 is a member of Rho family GTPases regulating cytoskeletal organization, and recent evidence also implicates Rac1 in exocytotic processes. Herein, we report that exposure of insulin-secreting (INS) cells to stimulatory glucose concentrations caused translocation of Rac1 from cytosol to the membrane fraction (including the plasmalemma), an indication of Rac1 activation. Furthermore, glucose stimulation increased Rac1 GTPase activity. Time course study indicates that such an effect is demonstrable only after 15 min stimulation with glucose. Expression of a dominant-negative Rac1 mutant (N17Rac1) abolished glucose-induced translocation of Rac1 and significantly inhibited insulin secretion stimulated by glucose and forskolin. This inhibitory effect on glucose-stimulated insulin secretion was more apparent in the late phase of secretion. However, N17Rac1 expression did not significantly affect insulin secretion induced by high K+. INS-1 cells expressing N17Rac1 also displayed significant morphological changes and disappearance of F-actin structures. Expression of wild-type Rac1 or a constitutively active Rac1 mutant (V12Rac1) did not significantly affect either the stimulated insulin secretion or basal release, suggesting that Rac1 activation is essential, but not sufficient, for evoking secretory process. These data suggest, for the first time, that Rac1 may be involved in glucose- and forskolin-stimulated insulin secretion, possibly at the level of recruitment of secretory granules through actin cytoskeletal network reorganization.

Nitrous acid (HONO) is a precursor of the hydroxyl radical (OH), a key oxidant in the degradation of most air pollutants. Field measurements indicate a large unknown source of HONO during the day time. Release of nitrous acid (HONO) from soil has been suggested as a major source of atmospheric HONO. We hypothesize that nitrite produced by biological nitrate reduction in oxygen-limited microzones in wet soils is a source of such HONO. Indeed, we found that various contrasting soil samples emitted HONO at high water-holding capacity (75-140%), demonstrating this to be a widespread phenomenon. Supplemental nitrate stimulated HONO emissions, whereas ethanol (70% v/v) treatment to minimize microbial activities reduced HONO emissions by 80%, suggesting that nitrate-dependent biotic processes are the sources of HONO. Highthroughput Illumina sequencing of 16S rRNA as well as functional gene transcripts associated with nitrate and nitrite reduction indicated that HONO emissions from soil samples were associated with nitrate reduction activities of diverse Proteobacteria. Incubation of pure cultures of bacterial nitrate reducers and gene-expression analyses, as well as the analyses of mutant strains deficient in nitrite reductases, showed positive correlations of HONO emissions with the capability of microbes to reduce nitrate to nitrite. Thus, we suggest biological nitrate reduction in oxygen-limited microzones as a hitherto unknown source of atmospheric HONO, affecting biogeochemical nitrogen cycling, atmospheric chemistry, and global modeling.

Background The coronavirus disease (COVID-19) has become an urgent and serious global public health crisis. Community engagement is the first line of defense in the fight against infectious diseases, and general practitioners (GPs) play an important role in it. GPs are facing unique challenges from disasters and pandemics in delivering health care. However, there is still no suitable mobile management system that can help GPs collect data, dynamically assess risks, and effectively triage or follow-up with patients with COVID-19. Objective The aim of this study is to design, develop, and deploy a mobile-based decision support system for COVID-19 (DDC19) to assist GPs in collecting data, assessing risk, triaging, managing, and following up with patients during the COVID-19 outbreak. Methods Based on the actual scenarios and the process of patients using health care, we analyzed the key issues that need to be solved and designed the main business flowchart of DDC19. We then constructed a COVID-19 dynamic risk stratification model with high recall and clinical interpretability, which was based on a multiclass logistic regression algorithm. Finally, through a 10-fold cross-validation to quantitatively evaluate the risk stratification ability of the model, a total of 2243 clinical data consisting of 36 dimension clinical features from fever clinics were used for training and evaluation of the model. Results DDC19 is composed of three parts: mobile terminal apps for the patient-end and GP-end, and the database system. All mobile terminal devices were wirelessly connected to the back end data center to implement request sending and data transmission. We used low risk, moderate risk, and high risk as labels, and adopted a 10-fold cross-validation method to evaluate and test the COVID-19 dynamic risk stratification model in different scenarios (different dimensions of personal clinical data accessible at an earlier stage). The data set dimensions were (2243, 15) when only using the data of patients’ demographic information, clinical symptoms, and contact history; (2243, 35) when the results of blood tests were added; and (2243, 36) after obtaining the computed tomography imaging results of the patient. The average value of the three classification results of the macro–area under the curve were all above 0.71 in each scenario. Conclusions DCC19 is a mobile decision support system designed and developed to assist GPs in providing dynamic risk assessments for patients with suspected COVID-19 during the outbreak, and the model had a good ability to predict risk levels in any scenario it covered.