METTL3-mediated m6A mRNA modification was involved in cadmium-induced liver injury.

Cadmium is an environmental pollutant that has extensive deleterious effects. However, the mechanisms underlying the hepatotoxicity induced by long-term exposure to cadmium remained undefined. In the present study, we explored the role of m6A methylation in the development of cadmium-induced liver disease. We showed a dynamic change of RNA methylation in liver tissue from mice administrated with cadmium chloride (CdCl2) for 3, 6 and 9 months, respectively. Particularly, the METTL3 expression was declined in a time-dependent manner, associated with the degree of liver injury, indicating the involvement of METTL3 in hepatotoxicity induced by CdCl2. Moreover, we established a mouse model with liver-specific over-expression of Mettl3 and administrated these mice with CdCl2 for 6 months. Notably, METTL3 highly expressed in hepatocytes attenuated CdCl2-induced steatosis and liver fibrosis in mice. In vitro assay also showed METTL3 overexpression ameliorated the CdCl2-induced cytotoxicity and activation of primary hepatic stellate cells. Furthermore, transcriptome analysis identified 268 differentially expressed genes both in mice liver tissue treated with CdCl2 for 3 months and 9 months. Among them, 115 genes were predicted to be regulated by METTL3 determined by m6A2Target database. Further analysis revealed the perturbation of metabolic pathway, glycerophospholipid metabolism, ErbB signaling pathway, Hippo signaling pathway, and choline metabolism in cancer, and circadian rhythm, led to hepatotoxicity induced by CdCl2. Collectively, our findings reveal new insight into the crucial role of epigenetic modifications in hepatic diseases caused by long-term exposure to cadmium.

The spatiotemporal inhomogeneity of pollutant concentrations and its dependence on regional weather conditions in a coastal city of China.

Hourly data for sulfur dioxide (SO2), nitrogen oxides (NOx), and inhalable particulate matter (PM10) over a 33-month period from a network of air quality monitoring stations across Qingdao, a major coastal city in eastern China, along with surface and upper-air meteorological data, are used to characterize the spatiotemporal variability of these pollutants in the region and the role of meteorological conditions play in pollution episodes. Large differences in the concentrations of all three pollutants are found between densely populated or industrial areas and suburban commercial or residential or coastal tourist areas, but the differences are relatively small between older and newer parts of the residential-commercial areas and between old and newly developed industrial areas. Wavelet analyses revealed a strong seasonal cycle for all three pollutants, introseasonal variability with a periodicity depending on pollutant and location, and diurnal and a semi-diurnal variability with season-dependent amplitude and phase. Low wind speed is found to be the leading factor for pollution buildup in the region. These results may prove useful for urban planning and development and implementation of effective air pollution control strategies for other coastal regions with economic development similar to Qingdao.

Trends in seasonal warm anomalies across the contiguous United States: Contributions from natural climate variability.

Many studies have shown the importance of anthropogenic greenhouse gas emissions in contributing to observed upward trends in the occurrences of temperature extremes over the U.S. However, few studies have investigated the contributions of internal variability in the climate system to these observed trends. Here we use daily maximum temperature time series from the North American Land Data Assimilation System Phase 2 (NLDAS-2) dataset to identify trends in seasonal warm anomalies over the contiguous U.S. in the three most recent decades and explore their relationships to low-frequency modes of internal climate variability. The results reveal substantial upward trends in the frequency of warm anomalies in all seasons and in all regions of the U.S., except for portions of the Intermountain West in winter where significant downward trends occur. The strengths and regional coverage of the trends, however, differ considerably by season. These trends can be explained, in part, by the large-scale anomalous atmospheric circulations associated with low-frequency sea-surface temperature oscillations characterized by the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO). The association between the upward trends in the seasonal warm anomalies and PDO and AMO is further confirmed by the century-long (1871-2012) Twentieth Century Reanalysis dataset.

Future changes in the climatology of the Great Plains low-level jet derived from fine resolution multi-model simulations.

The southerly Great Plains low-level jet (GPLLJ) is one of the most significant circulation features of the central U.S. linking large-scale atmospheric circulation with the regional climate. GPLLJs transport heat and moisture, contribute to thunderstorm and severe weather formation, provide a corridor for the springtime migration of birds and insects, enhance wind energy availability, and disperse air pollution. We assess future changes in GPLLJ frequency using an eight member ensemble of dynamically-downscaled climate simulations for the mid-21st century. Nocturnal GPLLJ frequency is projected to increase in the southern plains in spring and in the central plains in summer, whereas current climatological patterns persist into the future for daytime and cool season GPLLJs. The relationship between future GPLLJ frequency and the extent and strength of anticyclonic airflow over eastern North America varies with season. Most simulations project a westward shift of anticyclonic airflow in summer, but uncertainty is larger for spring with only half of the simulations suggesting a westward expansion. The choice of regional climate model and the driving lateral boundary conditions have a large influence on the projected future changes in GPLLJ frequency and highlight the importance of multi-model ensembles to estimate the uncertainty surrounding the future GPLLJ climatology.

Possible connections of the opposite trends in Arctic and Antarctic sea-ice cover.

Sea ice is an important component of the global climate system and a key indicator of climate change. A decreasing trend in Arctic sea-ice concentration is evident in recent years, whereas Antarctic sea-ice concentration exhibits a generally increasing trend. Various studies have investigated the underlying causes of the observed trends for each region, but possible linkages between the regional trends have not been studied. Here, we hypothesize that the opposite trends in Arctic and Antarctic sea-ice concentration may be linked, at least partially, through interdecadal variability of the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO). Although evaluation of this hypothesis is constrained by the limitations of the sea-ice cover record, preliminary statistical analyses of one short-term and two long-term time series of observed and reanalysis sea-ice concentrations data suggest the possibility of the hypothesized linkages. For all three data sets, the leading mode of variability of global sea-ice concentration is positively correlated with the AMO and negatively correlated with the PDO. Two wave trains related to the PDO and the AMO appear to produce anomalous surface-air temperature and low-level wind fields in the two polar regions that contribute to the opposite changes in sea-ice concentration.

Multi-year ozone concentration and its spectra in Shanghai, China.

The periodic properties of surface ozone variation were studied at five stations with different environmental conditions in Shanghai based on multi-year observations of ozone concentration and UV radiation using spectral decomposition methods. The spectra of surface ozone have distinct peaks at semi-diurnal, diurnal, intraseasonal, semiannual, annual, and quasi-biennial periods. The spectra for the frequency band larger than the semi-diurnal follow a -5/3 power law at all the stations. The diurnal peak values for all stations in different years are similar to each other, while the semi-diurnal peak values are somewhat different among the stations. The peak value of semi-diurnal cycle at the station Dongtan (ecological environment area) is smaller than that at the other stations. The monthly mean of surface ozone has a significant seasonal variation with a maximum in May, a secondary maximum in fall, a lower value in summer (July and August), and a minimum in December or January. However the seasonal variation of UV radiation monthly mean shows a relatively higher value in summer (July and August), and for other months it is closely related to the ozone monthly mean. These secondary peaks of the ozone monthly mean in fall might be caused by the UV radiation coming back to its relevant value after falling off during the Asia summer monsoon; it was not related to biomass burning. The intraseasonal cycling of ozone might be related to the MJO (Madden-Julian Oscillation). Further studies are needed to understand the relationship between the local ozone intraseasonal variation and the MJO. The quasi-biennial variation of ozone in Shanghai might be a local reflection of climate change and could be associated with ENSO (El-Nino Southern Oscillation). Further studies will be needed to understand the relationship of the quasi-biennial variation of ozone to ENSO.

Estimating contribution of wildland fires to ambient ozone levels in National Parks in the Sierra Nevada, California.

Data from four continuous ozone and weather monitoring sites operated by the National Park Service in Sierra Nevada, California, are used to develop an ozone forecasting model and to estimate the contribution of wildland fires on ambient ozone levels. The analyses of weather and ozone data pointed to the transport of ozone precursors from the Central Valley as an important source of pollution in these National Parks. Comparisons of forecasted and observed values demonstrated that accurate forecasts of next-day hourly ozone levels may be achieved by using a time series model with historic averages, expected local weather and modeled PM values as explanatory variables. Results on fire smoke influence indicated occurrence of significant increases in average ozone levels with increasing fire activity. The overall effect on diurnal ozone values, however, was small when compared with the amount of variability attributed to sources other than fire.