Differential Correlations Informed Metabolite Set Enrichment Analysis to Decipher Metabolic Heterogeneity of Disease.

Metabolic pathways are regarded as functional and basic components of the biological system. In metabolomics, metabolite set enrichment analysis (MSEA) is often used to identify the altered metabolic pathways (metabolite sets) associated with phenotypes of interest (POI), e.g., disease. However, in most studies, MSEA suffers from the limitation of low metabolite coverage. Random walk (RW)-based algorithms can be used to propagate the perturbation of detected metabolites to the undetected metabolites through a metabolite network model prior to MSEA. Nevertheless, most of the existing RW-based algorithms run on a general metabolite network constructed based on public databases, such as KEGG, without taking into consideration the potential influence of POI on the metabolite network, which may reduce the phenotypic specificities of the MSEA results. To solve this problem, a novel pathway analysis strategy, namely, differential correlation-informed MSEA (dci-MSEA), is proposed in this paper. Statistically, differential correlations between metabolites are used to evaluate the influence of POI on the metabolite network, so that a phenotype-specific metabolite network is constructed for RW-based propagation. The experimental results show that dci-MSEA outperforms the conventional RW-based MSEA in identifying the altered metabolic pathways associated with colorectal cancer. In addition, by incorporating the individual-specific metabolite network, the dci-MSEA strategy is easily extended to disease heterogeneity analysis. Here, dci-MSEA was used to decipher the heterogeneity of colorectal cancer. The present results highlight the clustering of colorectal cancer samples with their cluster-specific selection of differential pathways and demonstrate the feasibility of dci-MSEA in heterogeneity analysis. Taken together, the proposed dci-MSEA may provide insights into disease mechanisms and determination of disease heterogeneity.

Exploring the magnitude threshold of urban PM2.5 concentration: evidence from prefecture-level cities in China.

As major carriers of modern economy and population, cities and towns are vortex centers of pollution migration, and the environmental effects brought about by China's unprecedented urbanization can be imagined, although the specific scale is still a mystery. This paper focuses on the nonlinear response mechanism of urban PM2.5 concentration to the urbanization population scale, considering that China's urbanization development path is dominated by large- and medium-sized cities. The panel data of PM2.5 concentration of Chinese cities observed by satellite during 1998-2016 are used to capture the nonlinear characteristics of panel threshold model (PTM). The estimation results of the double-threshold PTM including the quadratic term of urbanization population show that the U-shaped relationship between urbanization population and PM2.5 concentration is nonlinear adjusted by urban GDP per capita with the two thresholds of 6777 Yuan and 10,296 Yuan at 2010 constant price. When the urban GDP per capita exceeds 10,296 Yuan, the urbanized population at the turning point of the U-shaped curve is 12.967 million people, which only appears in a few super-large cities such as Beijing, Tianjin, Shanghai and Chongqing. The size matching of urban economy and population is an important follow-up of environmental policies.

iMSEA: A Novel Metabolite Set Enrichment Analysis Strategy to Decipher Drug Interactions.

Drug combinations are commonly used to treat various diseases to achieve synergistic therapeutic effects or to alleviate drug resistance. Nevertheless, some drug combinations might lead to adverse effects, and thus, it is crucial to explore the mechanisms of drug interactions before clinical treatment. Generally, drug interactions have been studied using nonclinical pharmacokinetics, toxicology, and pharmacology. Here, we propose a complementary strategy based on metabolomics, which we call interaction metabolite set enrichment analysis, or iMSEA, to decipher drug interactions. First, a digraph-based heterogeneous network model was constructed to model the biological metabolic network based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Second, treatment-specific influences on all detected metabolites were calculated and propagated across the whole network model. Third, pathway activity was defined and enriched to quantify the influence of each treatment on the predefined functional metabolite sets, i.e., metabolic pathways. Finally, drug interactions were identified by comparing the pathway activity enriched by the drug combination treatments and the single drug treatments. A data set consisting of hepatocellular carcinoma (HCC) cells that were treated with oxaliplatin (OXA) and/or vitamin C (VC) was used to illustrate the effectiveness of the iMSEA strategy for evaluation of drug interactions. Performance evaluation using synthetic noise data was also performed to evaluate sensitivities and parameter settings for the iMSEA strategy. The iMSEA strategy highlighted synergistic effects of combined OXA and VC treatments including the alterations in the glycerophospholipid metabolism pathway and glycine, serine, and threonine metabolism pathway. This work provides an alternative method to reveal the mechanisms of drug combinations from the viewpoint of metabolomics.

Carbon mitigation performance of top-down administrative and fiscal decentralizations: Evidence from quasi-natural experiments in China's pilot counties.

Chinese-style decentralization reform not only creates miracles of economic growth, but also brings many unexpected gains, the most prominent of which are environmental externalities. The aim of this paper is to use the quasi-natural experiments of county-level administrative decentralization and fiscal decentralization reforms to study their heterogeneous effects on regional carbon emissions in the difference-in-difference framework. We selected the pilot counties as the treatment group from the full sample of 1981 counties during the period 1997-2017. The results show that the parallel trend test provides the empirical premise of the quasi-natural experiment. Due to the consistent estimation of instrumental variable estimation and PSM-DID methods, the average treatment effect of carbon emissions of the two kinds of decentralization reforms and their mixed reform pilots was obtained robustly. Consequently, the fiscal decentralization promotes carbon emission reduction, while administrative decentralization has insignificant effect. The mixed pilot counties that have carried out both administrative decentralization and fiscal decentralization reforms could usher in enhanced carbon reductions. To achieve more environmental benefits, policymakers need to further strengthen the coordination of county-level administrative and fiscal decentralization reforms.

DeepPSP: A Global-Local Information-Based Deep Neural Network for the Prediction of Protein Phosphorylation Sites.

Identification of phosphorylation sites is an important step in the function study and drug design of proteins. In recent years, there have been increasing applications of the computational method in the identification of phosphorylation sites because of its low cost and high speed. Most of the currently available methods focus on using local information around potential phosphorylation sites for prediction and do not take the global information of the protein sequence into consideration. Here, we demonstrated that the global information of protein sequences may be also critical for phosphorylation site prediction. In this paper, a new deep neural network model, called DeepPSP, was proposed for the prediction of protein phosphorylation sites. In the DeepPSP model, two parallel modules were introduced to extract both local and global features from protein sequences. Two squeeze-and-excitation blocks and one bidirectional long short-term memory block were introduced into each module to capture effective representations of the sequences. Comparative studies were carried out to evaluate the performance of DeepPSP, and four other prediction methods using public data sets The F1-score, area under receiver operating characteristic curves (AUROC), and area under precision-recall curves (AUPRC) of DeepPSP were found to be 0.4819, 0.82, and 0.50, respectively, for S/T general site prediction and 0.4206, 0.73, and 0.39, respectively, for Y general site prediction. Compared with the MusiteDeep method, the F1-score, AUROC, and AUPRC of DeepPSP were found to increase by 8.6, 2.5, and 8.7%, respectively, for S/T general site prediction and by 20.6, 5.8, and 18.2%, respectively, for Y general site prediction. Among the tested methods, the developed DeepPSP method was also found to produce best results for different kinase-specific site predictions including CDK, mitogen-activated protein kinase, CAMK, AGC, and CMGC. Taken together, the developed DeepPSP method may offer a more accurate phosphorylation site prediction by including global information. It may serve as an alternative model with better performance and interpretability for protein phosphorylation site prediction.

Revisiting the environmental Kuznets curve of PM2.5 concentration: evidence from prefecture-level and above cities of China.

In this paper, the panel data of China's four municipalities and 223 prefecture-level cities were used to investigate whether the EKC hypothesis for urban PM2.5 concentration was satisfied, considering such factors as urbanization population, electricity consumption, innovation capacity, and foreign direct investment in the cities. Assuming that the level of economic development directly affects the PM2.5 concentration, and the PM2.5 concentration will continue to increase at the early stage. Once the urban economy develops to a certain extent, the PM2.5 concentration will start to decline, and the environmental quality will be improved. Therefore, we attempt to construct the standard EKC by incorporating the quadratic and cubic terms of GDP per capita. The empirical results show that, except for the four municipalities of Beijing, Tianjin, Shanghai, and Chongqing, economic growth has a complex impact on PM2.5 concentration in most cities during the study period, rather than a simple inverted U-shaped pattern. Moreover, only in recent years has smog pollution shown an average decrease. But if the sources of smog are difficult to explore, it is worth considering the possibility of adjusting economic structure to meet environmental targets.

The environmental effect of capacity utilization in thermal power plants: evidence from interprovincial carbon emissions in China.

As the largest emitter of CO2 emissions, the installed capacity of thermal power generation in China is facing more and more strict restrictions, since the Chinese government proposed to dissolve overcapacity and intends to solve the problem of continuous reduction in utilization rate of electricity sector. Regretfully, the impact of power-generating capacity and its utilization on carbon emissions in the power sector has not yet been addressed. In this study, we incorporate the interaction between capacity and utilization of power sector into the dynamic spatial Durbin model, and estimate the specific impact on carbon dioxide emissions from the power sector based on the panel data set of China's provinces during 1991-2015. The results show that both installed capacity and utilization rate have positive effects on CO2 emissions. Interestingly, the estimation coefficient of their interaction term is negative, implying that the carbon emission reduction effect derives from the conflicting performance of capacity governance and utilization efficiency. Besides, the advantage of the emerging econometric method, the dynamic spatial Durbin model (SDM) with provinces and time-period fixed effects, is that it can estimate spatial interaction effects among the provinces and neighboring provinces and decompose those effects into two parts: long-term and short-term. However, the estimates indicate that only capacity has roughly significant spatial spillovers. As a result, dissolving overcapacity of thermal power generation and a necessary interprovincial coordination will promote carbon emission reduction rather than investing in coal-fired power plants, and the power authority should turn to alternative investment in cleaner power generation technologies.