MGDDI: A multi-scale graph neural networks for drug-drug interaction prediction.

Drug-drug interaction (DDI) prediction is crucial for identifying interactions within drug combinations, especially adverse effects due to physicochemical incompatibility. While current methods have made strides in predicting adverse drug interactions, limitations persist. Most methods rely on handcrafted features, restricting their applicability. They predominantly extract information from individual drugs, neglecting the importance of interaction details between drug pairs. To address these issues, we propose MGDDI, a graph neural network-based model for predicting potential adverse drug interactions. Notably, we use a multiscale graph neural network (MGNN) to learn drug molecule representations, addressing substructure size variations and preventing gradient issues. For capturing interaction details between drug pairs, we integrate a substructure interaction learning module based on attention mechanisms. Our experimental results demonstrate MGDDI's superiority in predicting adverse drug interactions, offering a solution to current methodological limitations.

Programmable RNA editing with compact CRISPR-Cas13 systems from uncultivated microbes.

Competitive coevolution between microbes and viruses has led to the diversification of CRISPR-Cas defense systems against infectious agents. By analyzing metagenomic terabase datasets, we identified two compact families (775 to 803 amino acids (aa)) of CRISPR-Cas ribonucleases from hypersaline samples, named Cas13X and Cas13Y. We engineered Cas13X.1 (775 aa) for RNA interference experiments in mammalian cell lines. We found Cas13X.1 could tolerate single-nucleotide mismatches in RNA recognition, facilitating prophylactic RNA virus inhibition. Moreover, a minimal RNA base editor, composed of engineered deaminase (385 aa) and truncated Cas13X.1 (445 aa), exhibited robust editing efficiency and high specificity to induce RNA base conversions. Our results suggest that there exist untapped bacterial defense systems in natural microbes that can function efficiently in mammalian cells, and thus potentially are useful for RNA-editing-based research.

A high-fidelity RNA-targeting Cas13 restores paternal Ube3a expression and improves motor functions in Angelman syndrome mice.

Angelman syndrome (AS) is a rare neurodevelopmental disorder caused by loss of function mutations in maternally expressed UBE3A. No gene-specific treatment is available for patients so far. Although intact and transcriptionally active, paternally inherited UBE3A is silenced by elongation of antisense long noncoding RNA UBE3A-ATS in neurons. Here, we demonstrated that RNA targeting of paternal Ube3a-ATS with a high-fidelity CRISPR-Cas13 (hfCas13x.1) system could restore Ube3a expression to similar levels as that of maternal Ube3a in the cultured mouse neurons. Furthermore, injection into lateral ventricles with neuron-specific hSyn1 promoter-driven hfCas13x.1 packaged in adeno-associated virus (AAV-PHP.eb) could restore paternal Ube3a expression in cortex and hippocampus of neonatal AS mice for up to 4 months after treatment. Behavioral tests showed that expression of paternal Ube3a significantly alleviated AS-related symptoms, including obesity and motor function. Our results suggested that hfCas13x.1-mediated suppression of the Ube3a-ATS lncRNA potentially serves as a promising targeted intervention for AS.

Vitamin K2 supplementation improves impaired glycemic homeostasis and insulin sensitivity for type 2 diabetes through gut microbiome and fecal metabolites.

BACKGROUND: There is insufficient evidence for the ability of vitamin K2 to improve type 2 diabetes mellitus symptoms by regulating gut microbial composition. Herein, we aimed to demonstrate the key role of the gut microbiota in the improvement of impaired glycemic homeostasis and insulin sensitivity by vitamin K2 intervention. METHODS: We first performed a 6-month RCT on 60 T2DM participants with or without MK-7 (a natural form of vitamin K2) intervention. In addition, we conducted a transplantation of the MK-7-regulated microbiota in diet-induced obesity mice for 4 weeks. 16S rRNA sequencing, fecal metabolomics, and transcriptomics in both study phases were used to clarify the potential mechanism. RESULTS: After MK-7 intervention, we observed notable 13.4%, 28.3%, and 7.4% reductions in fasting serum glucose (P = 0.048), insulin (P = 0.005), and HbA1c levels (P = 0.019) in type 2 diabetes participants and significant glucose tolerance improvement in diet-induced obesity mice (P = 0.005). Moreover, increased concentrations of secondary bile acids (lithocholic and taurodeoxycholic acid) and short-chain fatty acids (acetic acid, butyric acid, and valeric acid) were found in human and mouse feces accompanied by an increased abundance of the genera that are responsible for the biosynthesis of these metabolites. Finally, we found that 4 weeks of fecal microbiota transplantation significantly improved glucose tolerance in diet-induced obesity mice by activating colon bile acid receptors, improving host immune-inflammatory responses, and increasing circulating GLP-1 concentrations. CONCLUSIONS: Our gut-derived findings provide evidence for a regulatory role of vitamin K2 on glycemic homeostasis, which may further facilitate the clinical implementation of vitamin K2 intervention for diabetes management. TRIAL REGISTRATION: The study was registered at https://www.chictr.org.cn (ChiCTR1800019663).

Drivers of Increasing Ozone during the Two Phases of Clean Air Actions in China 2013-2020.

In response to the severe air pollution issue, the Chinese government implemented two phases (Phase I, 2013-2017; Phase II, 2018-2020) of clean air actions since 2013, resulting in a significant decline in fine particles (PM2.5) during 2013-2020, while the warm-season (April-September) mean maximum daily 8 h average ozone (MDA8 O3) increased by 2.6 µg m-3 yr-1 in China during the same period. Here, we derived the drivers behind the rising O3 concentrations during the two phases of clean air actions by using a bottom-up emission inventory, a regional chemical transport model, and a multiple linear regression model. We found that both meteorological variations (3.6 µg m-3) and anthropogenic emissions (6.7 µg m-3) contributed to the growth of MDA8 O3 from 2013 to 2020, with the changes in anthropogenic emissions playing a more important role. The anthropogenic contributions to the O3 rise during 2017-2020 (1.2 µg m-3) were much lower than that in 2013-2017 (5.2 µg m-3). The lack of volatile organic compound (VOC) control and the decline in nitrogen oxides (NOx) emissions were responsible for the O3 increase in 2013-2017 due to VOC-limited regimes in most urban areas, while the synergistic control of VOC and NOx in Phase II initially worked to mitigate O3 pollution during 2018-2020, although its effectiveness was offset by the penalty of PM2.5 decline. Future mitigation efforts should pay more attention to the simultaneous control of VOC and NOx to improve O3 air quality.

Transboundary health impacts of transported global air pollution and international trade.

Millions of people die every year from diseases caused by exposure to outdoor air pollution. Some studies have estimated premature mortality related to local sources of air pollution, but local air quality can also be affected by atmospheric transport of pollution from distant sources. International trade is contributing to the globalization of emission and pollution as a result of the production of goods (and their associated emissions) in one region for consumption in another region. The effects of international trade on air pollutant emissions, air quality and health have been investigated regionally, but a combined, global assessment of the health impacts related to international trade and the transport of atmospheric air pollution is lacking. Here we combine four global models to estimate premature mortality caused by fine particulate matter (PM2.5) pollution as a result of atmospheric transport and the production and consumption of goods and services in different world regions. We find that, of the 3.45 million premature deaths related to PM2.5 pollution in 2007 worldwide, about 12 per cent (411,100 deaths) were related to air pollutants emitted in a region of the world other than that in which the death occurred, and about 22 per cent (762,400 deaths) were associated with goods and services produced in one region for consumption in another. For example, PM2.5 pollution produced in China in 2007 is linked to more than 64,800 premature deaths in regions other than China, including more than 3,100 premature deaths in western Europe and the USA; on the other hand, consumption in western Europe and the USA is linked to more than 108,600 premature deaths in China. Our results reveal that the transboundary health impacts of PM2.5 pollution associated with international trade are greater than those associated with long-distance atmospheric pollutant transport.

Tracking PM2.5 and O3 Pollution and the Related Health Burden in China 2013-2020.

Based on the exposure data sets from the Tracking Air Pollution in China (TAP, http://tapdata.org.cn/), we characterized the spatiotemporal variations in PM2.5 and O3 exposures and quantified the long- and short-term exposure related premature deaths during 2013-2020 with respect to the two-stage clean air actions (2013-2017 and 2018-2020). We find a 48% decrease in national PM2.5 exposure during 2013-2020, although the decrease rate has slowed after 2017. At the same time, O3 pollution worsened, with the average April-September O3 exposure increased by 17%. The improved air quality led to 308 thousand and 16 thousand avoided long- and short-term exposure related deaths, respectively, in 2020 compared to the 2013 level, which was majorly attributed to the reduction in ambient PM2.5 concentration. It is also noticed that with smaller PM2.5 reduction, the avoided long-term exposure associated deaths in 2017-2020 (13%) was greater than that in 2013-2017 (9%), because the exposure-response curve is nonlinear. As a result of the efforts in reducing PM2.5-polluted days with the daily average PM2.5 higher than 75 µg/m3 and the considerable increase in O3-polluted days with the daily maximum 8 h average O3 higher than 160 µg/m3, deaths attributable to the short-term O3 exposure were greater than those due to PM2.5 exposure since 2018. Future air quality improvement strategies for the coordinated control of PM2.5 and O3 are urgently needed.

Tracking Daily Concentrations of PM2.5 Chemical Composition in China since 2000.

PM2.5 chemical components play significant roles in the climate, air quality, and public health, and the roles vary due to their different physicochemical properties. Obtaining accurate and timely updated information on China's PM2.5 chemical composition is the basis for research and environmental management. Here, we developed a full-coverage near-real-time PM2.5 chemical composition data set at 10 km spatial resolution since 2000, combining the Weather Research and Forecasting-Community Multiscale Air Quality modeling system, ground observations, a machine learning algorithm, and multisource-fusion PM2.5 data. PM2.5 chemical components in our data set are in good agreement with the available observations (correlation coefficients range from 0.64 to 0.75 at a monthly scale from 2000 to 2020 and from 0.67 to 0.80 at a daily scale from 2013 to 2020; most normalized mean biases within ±20%). Our data set reveals the long-term trends in PM2.5 chemical composition in China, especially the rapid decreases after 2013 for sulfate, nitrate, ammonium, organic matter, and black carbon, at the rate of -9.0, -7.2, -8.1, -8.4, and -9.2% per year, respectively. The day-to-day variability is also well captured, including evolutions in spatial distribution and shares of PM2.5 components. As part of Tracking Air Pollution in China (http://tapdata.org.cn), this daily-updated data set provides large opportunities for health and climate research as well as policy-making in China.

Drivers of improved PM2.5 air quality in China from 2013 to 2017.

From 2013 to 2017, with the implementation of the toughest-ever clean air policy in China, significant declines in fine particle (PM2.5) concentrations occurred nationwide. Here we estimate the drivers of the improved PM2.5 air quality and the associated health benefits in China from 2013 to 2017 based on a measure-specific integrated evaluation approach, which combines a bottom-up emission inventory, a chemical transport model, and epidemiological exposure-response functions. The estimated national population-weighted annual mean PM2.5 concentrations decreased from 61.8 (95%CI: 53.3-70.0) to 42.0 µg/m3 (95% CI: 35.7-48.6) in 5 y, with dominant contributions from anthropogenic emission abatements. Although interannual meteorological variations could significantly alter PM2.5 concentrations, the corresponding effects on the 5-y trends were relatively small. The measure-by-measure evaluation indicated that strengthening industrial emission standards (power plants and emission-intensive industrial sectors), upgrades on industrial boilers, phasing out outdated industrial capacities, and promoting clean fuels in the residential sector were major effective measures in reducing PM2.5 pollution and health burdens. These measures were estimated to contribute to 6.6- (95% CI: 5.9-7.1), 4.4- (95% CI: 3.8-4.9), 2.8- (95% CI: 2.5-3.0), and 2.2- (95% CI: 2.0-2.5) µg/m3 declines in the national PM2.5 concentration in 2017, respectively, and further reduced PM2.5-attributable excess deaths by 0.37 million (95% CI: 0.35-0.39), or 92% of the total avoided deaths. Our study confirms the effectiveness of China's recent clean air actions, and the measure-by-measure evaluation provides insights into future clean air policy making in China and in other developing and polluting countries.

Clean air actions in China, PM2.5 exposure, and household medical expenditures: A quasi-experimental study.

BACKGROUND: Exposure to air pollution, a leading contributor to the global burden of disease, can cause economic losses. Driven by clean air policies, the air quality in China, one of the most polluted countries, has improved rapidly since 2013. This has enabled a unique, quasi-experiment to assess the economic impact of air pollution empirically. METHODS AND FINDINGS: Using a series of nation-scale longitudinal surveys in 2011, 2013, and 2015, we first examined the questionnaire-based medical expenditure changes before and after the policy intervention for air pollution. Using a state-of-the-art estimator of the historical concentration of particulate matters with diameter less than 2.5 µm (particulate matter (PM)2.5), we further quantified the association between household medical expenditure and PM2.5 using mixed-effect models of the repeated measurements from 26,511 households in 126 cities. Regression models suggest a robust linear association between reduced PM2.5 and saved medical expenditures, since the association did not vary significantly across models with different covariate adjustments, subregions, or subpopulations. Each 10 µg/m3 reduction in PM2.5 was associated with a saving of 251.6 (95% CI: 30.8, 472.3; p-value = 0.026) Yuan in per capita annual medical expenditure. However, due to limitations in data quality (e.g., self-reported expenditures), and imperfect control for unmeasured confounders or impact from concurrent healthcare reform in China, the causality underlying our findings should be further confirmed or refuted. CONCLUSION: In this study, we observed that compared with the PM2.5 reduction in 2013, the PM2.5 reduction in 2017 was associated with a saving of 552 (95% CI: 68, 1036) Yuan / (person × year), or approximately 736 billion Yuan (equivalent to 111 billion US dollar) per year nationally, which is equivalent to approximately 1% of the national gross domestic product of China.

tRNA derived fragment (tRF)-3009 participates in modulation of IFN-α-induced CD4+ T cell oxidative phosphorylation in lupus patients.

BACKGROUND: Accumulating evidence suggests tRNA-derived fragments (tRFs) play important roles in cellular homeostasis. Here we aimed to explore aberrant expression of tRFs in CD4+ T cells from patients with systemic lupus erythematosus (SLE) and their potential function in the SLE pathogenesis. METHODS: First, small RNA sequencing was performed on CD4+ T cells from four SLE patients and three healthy controls (HCs). Candidate tRFs were then validated in CD4+ T cells from 97 SLE patients and their relevant disease controls using qRT-PCR. Then sequencing was used to investigate the profiles of HC-derived CD4+ T cells transfected with tRF-3009. Lastly, tRF-3009 siRNA or tRF-3009 mimics were transfected into CD4+ T cells with/without IFN-α. Changes in oxygen consumption rate (OCR), ATP, and ROS production were analyzed. RESULTS: We identified 482 differentially expressed tRFs from SLE CD4+ T cells and chose tRF-3009 for further analysis due to its upregulation and the positive correlations between its expression and SLEDAI, active lupus nephritis and serum IFN-α levels. In vitro, tRF-3009 over-expressing CD4+ T cell profiling and putative analysis linked this product to the type I IFN and oxidative phosphorylation (OXPHOS) pathways. Interestingly, IFN-α is capable of inducing ROS and ATP production in CD4+ T cells, while knockdown of tRF-3009 reversed this process. Overexpression of tRF-3009 in CD4+ T cells alone was sufficient to upregulate OCR, ROS, and ATP production. CONCLUSIONS: Our study is the first to link aberrant tRF expression and SLE. tRF-3009 may participate in metabolic modulation of IFN-α-induced CD4+ T cell OXPHOS in lupus.

Air quality and health benefits of China's current and upcoming clean air policies.

China is currently in a crucial stage of air pollution control and has intensive clean air policies. Past strict policies have demonstrated remarkable effectiveness in emission control and fine particulate matter (PM2.5) pollution mitigation; however, it is not clear what the continuous benefits of current policies are for the future. Here, we summarize China's currently implemented, released, and upcoming clean air policies and estimate the air quality and health benefits of the implementation of these policies until 2030. We found that China's current and upcoming clean air policies could reduce major pollutant emissions by 14.3-70.5% under continued socio-economic growth from 2010 to 2030. These policies could decrease the national population-weighted PM2.5 concentrations from 61.6 µg m-3 in 2010 to 26.4 µg m-3 in 2030 (57.2% reduction). These air quality improvements will ensure that over 80% of the population lives in areas with PM2.5 levels below the current annual PM2.5 air quality standard (i.e., 35 µg m-3) and will avoid 95.0 (95% CI, 76.3, 104.2) thousand premature deaths in 2030. We also point out several inadequacies of current clean air policies, suggesting that more ambitious control actions are needed to better protect public health with an increasing ageing population. Our findings could provide quantitative insights that can be used to better address air pollution issues in China and other developing countries.

Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion.

Air pollution has altered the Earth's radiation balance, disturbed the ecosystem, and increased human morbidity and mortality. Accordingly, a full-coverage high-resolution air pollutant data set with timely updates and historical long-term records is essential to support both research and environmental management. Here, for the first time, we develop a near real-time air pollutant database known as Tracking Air Pollution in China (TAP, http://tapdata.org.cn/) that combines information from multiple data sources, including ground observations, satellite aerosol optical depth (AOD), operational chemical transport model simulations, and other ancillary data such as meteorological fields, land use data, population, and elevation. Daily full-coverage PM2.5 data at a spatial resolution of 10 km is our first near real-time product. The TAP PM2.5 is estimated based on a two-stage machine learning model coupled with the synthetic minority oversampling technique and a tree-based gap-filling method. Our model has an averaged out-of-bag cross-validation R2 of 0.83 for different years, which is comparable to those of other studies, but improves its performance at high pollution levels and fills the gaps in missing AOD on daily scale. The full coverage and near real-time updates of the daily PM2.5 data allow us to track the day-to-day variations in PM2.5 concentrations over China in a timely manner. The long-term records of PM2.5 data since 2000 will also support policy assessments and health impact studies. The TAP PM2.5 data are publicly available through our website for sharing with the research and policy communities.

High glucose inhibits proliferation and differentiation of osteoblast in alveolar bone by inducing pyroptosis.

The inhibition of high glucose on the proliferation and differentiation of osteoblast in alveolar bone are well documented. However, a comprehensive study focused on the molecular mechanisms is still unknown. Recent studies have revealed that caspase-1 participates in the pathological processes of hepatic injury, cancers and diabetes related complications. However, the relationship between pyroptosis and proliferation and differentiation of osteoblasts has not been investigated. This study aimed to explore the possible pyroptosis participating in the inhibition of high glucose on the proliferation and differentiation of osteoblast in alveolar bone. The diabetes model was constructed both in vitro and in vivo to detect the expression of pyroptosis related factors. These results show that high glucose inhibits proliferation and differentiation of osteoblast in alveolar bone through pyroptosis pathway. Furthermore, caspase-1 inhibitor was co-administered with high glucose in ME3T3-E1 cells, which shows that caspase-1 inhibitor could repress effect of high glucose on the proliferation and differentiation of osteoblast. In conclusion, High glucose could activate the pyroptosis through the caspase-1/GSDMD/IL-1ß pathway to inhibit the proliferation and differentiation of osteoblast in alveolar bone, which provides a theoretical basis for clinical treatment of alveolar bone disease in diabetic patients.

Estimating Spatiotemporal Variation in Ambient Ozone Exposure during 2013-2017 Using a Data-Fusion Model.

Since 2013, clean-air actions in China have reduced ambient concentrations of PM2.5. However, recent studies suggest that ground surface O3 concentrations increased over the same period. To understand the shift in air pollutants and to comprehensively evaluate their impacts on health, a spatiotemporal model for O3 is required for exposure assessment. This study presents a data-fusion algorithm for O3 estimation that combines in situ observations, satellite remote sensing measurements, and model results from the community multiscale air quality model. Performance of the algorithm for O3 estimation was evaluated by five-fold cross-validation. The estimates are highly correlated with the in situ observations of the maximum daily 8 h averaged O3 (R2 = 0.70). The mean modeling error (measured using the root-mean-squared error) is 26 µg/m3, which accounts for 29% of the mean level. We also found that satellite O3 played a key role to improve model performance, particularly during warm months. The estimates were further used to illustrate spatiotemporal variation in O3 during 2013-2017 for the whole country. In contrast to the reduced trend of PM2.5, we found that the population-weighted O3 mean increased from 86 µg/m3 in 2013 to 95 µg/m3 in 2017, with a rate of 2.07 (95% CI: 1.65, 2.48) µg/m3 per year at the national level. This increased trend in O3 suggests that it is becoming an important contributor to the burden of diseases attributable to air pollutants in China. The developed method and the results generated from this study can be used to support future health-related studies in China.

Air pollution characteristics and their relationship with emissions and meteorology in the Yangtze River Delta region during 2014-2016.

With rapid economic growth and urbanization, the Yangtze River Delta (YRD) region in China has experienced serious air pollution challenges. In this study, we analyzed the air pollution characteristics and their relationship with emissions and meteorology in the YRD region during 2014-2016. In recent years, the concentrations of all air pollutants, except O3, decreased. Spatially, the PM2.5, PM10, SO2, and CO concentrations were higher in the northern YRD region, and NO2 and O3 were higher in the central YRD region. Based on the number of non-attainment days (i.e., days with air quality index greater than 100), PM2.5 was the largest contributor to air pollution in the YRD region, followed by O3, PM10, and NO2. However, particulate matter pollution has declined gradually, while O3 pollution worsened. Meteorological conditions mainly influenced day-to-day variations in pollutant concentrations. PM2.5 concentration was inversely related to wind speed, while O3 concentration was positively correlated with temperature and negatively correlated with relative humidity. The air quality improvement in recent years was mainly attributed to emission reductions. During 2014-2016, PM2.5, PM10, SO2, NOx, CO, NH3, and volatile organic compound (VOC) emissions in the YRD region were reduced by 26.3%, 29.2%, 32.4%, 8.1%, 15.9%, 4.5%, and 0.3%, respectively. Regional transport also contributed to the air pollution. During regional haze periods, pollutants from North China and East China aggravated the pollution in the YRD region. Our findings suggest that emission reduction and regional joint prevention and control helped to improve the air quality in the YRD region.

An Ensemble Machine-Learning Model To Predict Historical PM2.5 Concentrations in China from Satellite Data.

The long satellite aerosol data record enables assessments of historical PM2.5 level in regions where routine PM2.5 monitoring began only recently. However, most previous models reported decreased prediction accuracy when predicting PM2.5 levels outside the model-training period. In this study, we proposed an ensemble machine learning approach that provided reliable PM2.5 hindcast capabilities. The missing satellite data were first filled by multiple imputation. Then the modeling domain, China, was divided into seven regions using a spatial clustering method to control for unobserved spatial heterogeneity. A set of machine learning models including random forest, generalized additive model, and extreme gradient boosting were trained in each region separately. Finally, a generalized additive ensemble model was developed to combine predictions from different algorithms. The ensemble prediction characterized the spatiotemporal distribution of daily PM2.5 well with the cross-validation (CV) R2 (RMSE) of 0.79 (21 µg/m3). The cluster-based subregion models outperformed national models and improved the CV R2 by ∼0.05. Compared with previous studies, our model provided more accurate out-of-range predictions at the daily level ( R2 = 0.58, RMSE = 29 µg/m3) and monthly level ( R2 = 0.76, RMSE = 16 µg/m3). Our hindcast modeling system allows for the construction of unbiased historical PM2.5 levels.

Current Emissions and Future Mitigation Pathways of Coal-Fired Power Plants in China from 2010 to 2030.

As the largest energy infrastructure in China, the power sector consumed approximately half of China's coal over the past decade and threatened air quality and greenhouse gas (GHG) abatement targets. In this work, we assessed the evolution of coal-fired power plants and associated emissions in China during 2010-2030 by using a unit-based emission projection model, which integrated the historical power plant information, turnover of the future power plant fleet, and evolution of end-of-pipe control technologies. We found that, driven by stringent environmental legislation, SO2, NO x, and PM2.5 (particulate matter less than 2.5 µm in diameter) emissions from coal-fired power plants decreased by 49%, 45%, and 24%, respectively, during 2010-2015, compared to 15% increase in CO2 emissions. In contrast to ever-increasing CO2 emissions until 2030 under current energy development planning, we found that aggressive energy development planning could curb CO2 emissions from the peak before 2030. Owing to the implementation of a "near zero" emission control policy, we projected emissions of air pollutants will significantly decrease during 2016-2030. Early retirement of small and low-efficiency power plants would further reduce air pollutants and CO2 emissions. Our study explored various mitigation pathways for China's coal-fired power plants, which could reduce coal consumption, air pollutants, and CO2 emissions and improve energy efficiency.