Silybin Restored CYP3A Expression through the Sirtuin 2/Nuclear Factor κ-B Pathway in Mouse Nonalcoholic Fatty Liver Disease.

Silybin is widely used as a hepatoprotective agent in various liver disease therapies and has been previously identified as a CYP3A inhibitor. However, little is known about the effect of silybin on CYP3A and the regulatory mechanism during high-fat-diet (HFD)-induced liver inflammation. In our study, we found that silybin restored CYP3A expression and activity that were decreased by HFD and conditioned medium (CM) from palmitate-treated Kupffer cells. Moreover, silybin suppressed liver inflammation in HFD-fed mice and inhibited nuclear factor κ-B translocation into the nucleus through elevation of SIRT2 expression and promotion of p65 deacetylation. This effect was confirmed by overexpression of SIRT2, which suppressed p65 nuclear translocation and restored CYP3A transcription affected by CM. The hepatic NAD+ concentration markedly decreased in HFD-fed mice and CM-treated hepatocytes/HepG2 cells but increased after silybin treatment. Supplementing nicotinamide mononucleotide as an NAD+ donor inhibited p65 acetylation, decreased p65 nuclear translocation, and restored cyp3a transcription in both HepG2 cells and mouse hepatocytes. These results suggest that silybin regulates metabolic enzymes during liver inflammation by a mechanism related to the increase in NAD+ and SIRT2 levels. In addition, silybin enhanced the intracellular NAD+ concentration by decreasing poly-ADP ribosyl polymerase-1 expression. In summary, silybin increased NAD+ concentration, promoted SIRT2 expression, and lowered p65 acetylation both in vivo and in vitro, which supported the recovery of CYP3A expression. These findings indicate that the NAD+/SIRT2 pathway plays an important role in CYP3A regulation during nonalcoholic fatty liver disease. SIGNIFICANCE STATEMENT: This research revealed the differential regulation of CYP3A by silybin under physiological and fatty liver pathological conditions. In the treatment of nonalcoholic fatty liver disease, silybin restored, not inhibited, CYP3A expression and activity through the NAD+/ sirtuin 2 pathway in accordance with its anti-inflammatory effect.

Large-scale determinants of street tree growth rates across an urban environment.

Urban street trees offer cities critical environmental and social benefits. In New York City (NYC), a decadal census of every street tree is conducted to help understand and manage the urban forest. However, it has previously been impossible to analyze growth of an individual tree because of uncertainty in tree location. This study overcomes this limitation using a three-step alignment process for identifying individual trees with ZIP Codes, address, and species instead of map coordinates. We estimated individual growth rates for 126,362 street trees (59 species and 19% of 2015 trees) using the difference between diameter at breast height (DBH) from the 2005 and 2015 tree censuses. The tree identification method was verified by locating and measuring the DBH of select trees and measuring a set of trees annually for over 5 years. We examined determinants of tree growth rates and explored their spatial distribution. In our newly created NYC tree growth database, fourteen species have over 1000 unique trees. The three most abundant tree species vary in growth rates; London Planetree (n = 32,056, 0.163 in/yr) grew the slowest compared to Honeylocust (n = 15,967, 0.356 in/yr), and Callery Pear (n = 15,902, 0.334 in/yr). Overall, Silver Linden was the fastest growing species (n = 1,149, 0.510 in/yr). Ordinary least squares regression that incorporated biological factors including size and the local urban form indicated that species was the major factor controlling growth rates, and tree stewardship had only a small effect. Furthermore, tree measurements by volunteer community scientists were as accurate as those made by NYC staff. Examining city wide patterns of tree growth indicates that areas with a higher Social Vulnerability Index have higher than expected growth rates. Continued efforts in street tree planting should utilize known growth rates while incorporating community voices to better provide long-term ecosystem services across NYC.

Change of urban park usage as a response to the COVID-19 global pandemic.

Urban parks became critical for maintaining the well-being of urban residents during the COVID-19 global pandemic. To examine the impact of COVID-19 on urban park usage, we selected New York City (NYC) and used SafeGraph mobility data, which was collected from a large sample of mobile phone users, to assess the change in park visits and travel distance to a park based on 1) park type, 2) the income level of the visitor census block group (visitor CBG) and 3) that of the park census block group (park CBG). All analyses were adjusted for the impact of temperature on park visitation, and we focused primarily on visits made by NYC residents. Overall, for the eight most popular park types in NYC, visits dropped by 49.2% from 2019 to 2020. The peak reduction in visits occurred in April 2020. Visits to all park types, excluding Nature Areas, decreased from March to December 2020 as compared to 2019. Parks located in higher-income CBGs tended to have lower reductions in visits, with this pattern being primarily driven by large parks, including Flagship Parks, Community Parks and Nature Areas. All types of parks saw significant decreases in distance traveled to visit them, with the exception of the Jointly Operated Playground, Playground, and Nature Area park types. Visitors originating from lower-income CBGs traveled shorter distances to parks and had less reduction in travel distances compared to those from higher-income CBGs. Furthermore, both before and during the pandemic, people tended to travel a greater distance to parks located in high-income CBGs compared to those in low-income CBGs. Finally, multiple types of parks proved crucial destinations for NYC residents during the pandemic. This included Nature Areas to which the visits remained stable, along with Recreation Field/Courts which had relatively small decreases in visits, especially for lower-income communities. Results from this study can support future park planning by shedding light on the different uses of certain park types before and during a global crisis, when access to these facilities can help alleviate the human well-being consequences of "lockdown" policies.

Prediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy.

Liver has an ability to regenerate itself in mammals, whereas the mechanism has not been fully explained. Here we used a GC/MS-based metabolomic method to profile the dynamic endogenous metabolic change in the serum of C57BL/6J mice at different times after 2/3 partial hepatectomy (PHx), and nine machine learning methods including Least Absolute Shrinkage and Selection Operator Regression (LASSO), Partial Least Squares Regression (PLS), Principal Components Regression (PCR), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (xgbDART), Neural Network (NNET) and Bayesian Regularized Neural Network (BRNN) were used for regression between the liver index and metabolomic data at different stages of liver regeneration. We found a tree-based random forest method that had the minimum average Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and the maximum R square (R2) and is time-saving. Furthermore, variable of importance in the project (VIP) analysis of RF method was performed and metabolites with VIP ranked top 20 were selected as the most critical metabolites contributing to the model. Ornithine, phenylalanine, 2-hydroxybutyric acid, lysine, etc. were chosen as the most important metabolites which had strong correlations with the liver index. Further pathway analysis found Arginine biosynthesis, Pantothenate and CoA biosynthesis, Galactose metabolism, Valine, leucine and isoleucine degradation were the most influenced pathways. In summary, several amino acid metabolic pathways and glucose metabolism pathway were dynamically changed during liver regeneration. The RF method showed advantages for predicting the liver index after PHx over other machine learning methods used and a metabolic clock containing four metabolites is established to predict the liver index during liver regeneration.

An international application of the city-wide mobile noise mapping methodology: Retro-active traffic attribution on a bicycle commuters health study in New York City.

The spatial resolution of third party traffic data is not adequately describing the variation of air pollution exposure along the travelled routes of bicycle commuters. In prior work, a city-wide mobile noise mapping methodology was proposed to predict Black Carbon exposure for random bicycle trips, including meteorological variability. In a proof-of-concept pilot, funded by the National Institutes of Environmental Health Sciences (NIEHS), this method is examined in the context of a commuter study in New York City. An independent measurement campaign sampled for noise, Black Carbon and Ultrafine Particles in NYC. We focus on the spatiotemporal analysis of the preliminary data. NYC has different fleet composition compared to Ghent (i.e. less diesel, more hybrids) and different geography. Additional parameters are identified to improve the model in comparison to the prior European work. The validity, feasibility and applicability of the methodology are positively evaluated. Sampling exposure across all seasons during rush hours couldn't be reached within the pilot. Adding noise levels meters to the protocol of the commuter study can supply the missing data with minimal investments. When a full year of data becomes available, the commuter study can be retro-actively attributed with meteorology independent exposure for BC and UFP.