Fertilizer management for global ammonia emission reduction.

Crop production is a large source of atmospheric ammonia (NH3), which poses risks to air quality, human health and ecosystems1-5. However, estimating global NH3 emissions from croplands is subject to uncertainties because of data limitations, thereby limiting the accurate identification of mitigation options and efficacy4,5. Here we develop a machine learning model for generating crop-specific and spatially explicit NH3 emission factors globally (5-arcmin resolution) based on a compiled dataset of field observations. We show that global NH3 emissions from rice, wheat and maize fields in 2018 were 4.3 ± 1.0 Tg N yr-1, lower than previous estimates that did not fully consider fertilizer management practices6-9. Furthermore, spatially optimizing fertilizer management, as guided by the machine learning model, has the potential to reduce the NH3 emissions by about 38% (1.6 ± 0.4 Tg N yr-1) without altering total fertilizer nitrogen inputs. Specifically, we estimate potential NH3 emissions reductions of 47% (44-56%) for rice, 27% (24-28%) for maize and 26% (20-28%) for wheat cultivation, respectively. Under future climate change scenarios, we estimate that NH3 emissions could increase by 4.0 ± 2.7% under SSP1-2.6 and 5.5 ± 5.7% under SSP5-8.5 by 2030-2060. However, targeted fertilizer management has the potential to mitigate these increases.

Changes in above- versus belowground biomass distribution in permafrost regions in response to climate warming.

Permafrost regions contain approximately half of the carbon stored in land ecosystems and have warmed at least twice as much as any other biome. This warming has influenced vegetation activity, leading to changes in plant composition, physiology, and biomass storage in aboveground and belowground components, ultimately impacting ecosystem carbon balance. Yet, little is known about the causes and magnitude of long-term changes in the above- to belowground biomass ratio of plants (η). Here, we analyzed η values using 3,013 plots and 26,337 species-specific measurements across eight sites on the Tibetan Plateau from 1995 to 2021. Our analysis revealed distinct temporal trends in η for three vegetation types: a 17% increase in alpine wetlands, and a decrease of 26% and 48% in alpine meadows and alpine steppes, respectively. These trends were primarily driven by temperature-induced growth preferences rather than shifts in plant species composition. Our findings indicate that in wetter ecosystems, climate warming promotes aboveground plant growth, while in drier ecosystems, such as alpine meadows and alpine steppes, plants allocate more biomass belowground. Furthermore, we observed a threefold strengthening of the warming effect on η over the past 27 y. Soil moisture was found to modulate the sensitivity of η to soil temperature in alpine meadows and alpine steppes, but not in alpine wetlands. Our results contribute to a better understanding of the processes driving the response of biomass distribution to climate warming, which is crucial for predicting the future carbon trajectory of permafrost ecosystems and climate feedback.

Climate change increases carbon allocation to leaves in early leaf green-up.

Global greening, characterized by an increase in leaf area index (LAI), implies an increase in foliar carbon (C). Whether this increase in foliar C under climate change is due to higher photosynthesis or to higher allocation of C to leaves remains unknown. Here, we explored the trends in foliar C accumulation and allocation during leaf green-up from 2000 to 2017 using satellite-derived LAI and solar-induced chlorophyll fluorescence (SIF) across the Northern Hemisphere. The accumulation of foliar C accelerated in the early green-up period due to both increased photosynthesis and higher foliar C allocation driven by climate change. In the late stage of green-up, however, we detected decreasing trends in foliar C accumulation and foliar C allocation. Such stage-dependent trends in the accumulation and allocation of foliar C are not represented in current terrestrial biosphere models. Our results highlight that a better representation of C allocation should be incorporated into models.

The detection and attribution of extreme reductions in vegetation growth across the global land surface.

Negative extreme anomalies in vegetation growth (NEGs) usually indicate severely impaired ecosystem services. These NEGs can result from diverse natural and anthropogenic causes, especially climate extremes (CEs). However, the relationship between NEGs and many types of CEs remains largely unknown at regional and global scales. Here, with satellite-derived vegetation index data and supporting tree-ring chronologies, we identify periods of NEGs from 1981 to 2015 across the global land surface. We find 70% of these NEGs are attributable to five types of CEs and their combinations, with compound CEs generally more detrimental than individual ones. More importantly, we find that dominant CEs for NEGs vary by biome and region. Specifically, cold and/or wet extremes dominate NEGs in temperate mountains and high latitudes, whereas soil drought and related compound extremes are primarily responsible for NEGs in wet tropical, arid and semi-arid regions. Key characteristics (e.g., the frequency, intensity and duration of CEs, and the vulnerability of vegetation) that determine the dominance of CEs are also region- and biome-dependent. For example, in the wet tropics, dominant individual CEs have both higher intensity and longer duration than non-dominant ones. However, in the dry tropics and some temperate regions, a longer CE duration is more important than higher intensity. Our work provides the first global accounting of the attribution of NEGs to diverse climatic extremes. Our analysis has important implications for developing climate-specific disaster prevention and mitigation plans among different regions of the globe in a changing climate.

Accounting for herbaceous communities in process-based models will advance our understanding of "grassy" ecosystems.

Grassland and other herbaceous communities cover significant portions of Earth's terrestrial surface and provide many critical services, such as carbon sequestration, wildlife habitat, and food production. Forecasts of global change impacts on these services will require predictive tools, such as process-based dynamic vegetation models. Yet, model representation of herbaceous communities and ecosystems lags substantially behind that of tree communities and forests. The limited representation of herbaceous communities within models arises from two important knowledge gaps: first, our empirical understanding of the principles governing herbaceous vegetation dynamics is either incomplete or does not provide mechanistic information necessary to drive herbaceous community processes with models; second, current model structure and parameterization of grass and other herbaceous plant functional types limits the ability of models to predict outcomes of competition and growth for herbaceous vegetation. In this review, we provide direction for addressing these gaps by: (1) presenting a brief history of how vegetation dynamics have been developed and incorporated into earth system models, (2) reporting on a model simulation activity to evaluate current model capability to represent herbaceous vegetation dynamics and ecosystem function, and (3) detailing several ecological properties and phenomena that should be a focus for both empiricists and modelers to improve representation of herbaceous vegetation in models. Together, empiricists and modelers can improve representation of herbaceous ecosystem processes within models. In so doing, we will greatly enhance our ability to forecast future states of the earth system, which is of high importance given the rapid rate of environmental change on our planet.

Resolving the Dust Bowl paradox of grassland responses to extreme drought.

During the 1930s Dust Bowl drought in the central United States, species with the C3 photosynthetic pathway expanded throughout C4-dominated grasslands. This widespread increase in C3 grasses during a decade of low rainfall and high temperatures is inconsistent with well-known traits of C3 vs. C4 pathways. Indeed, water use efficiency is generally lower, and photosynthesis is more sensitive to high temperatures in C3 than C4 species, consistent with the predominant distribution of C3 grasslands in cooler environments and at higher latitudes globally. We experimentally imposed extreme drought for 4 y in mixed C3/C4 grasslands in Kansas and Wyoming and, similar to Dust Bowl observations, also documented three- to fivefold increases in C3/C4 biomass ratios. To explain these paradoxical responses, we first analyzed long-term climate records to show that under nominal conditions in the central United States, C4 grasses dominate where precipitation and air temperature are strongly related (warmest months are wettest months). In contrast, C3 grasses flourish where precipitation inputs are less strongly coupled to warm temperatures. We then show that during extreme drought years, precipitation-temperature relationships weaken, and the proportion of precipitation falling during cooler months increases. This shift in precipitation seasonality provides a mechanism for C3 grasses to respond positively to multiyear drought, resolving the Dust Bowl paradox. Grasslands are globally important biomes and increasingly vulnerable to direct effects of climate extremes. Our findings highlight how extreme drought can indirectly alter precipitation seasonality and shift ecosystem phenology, affecting function in ways not predictable from key traits of C3 and C4 species.

Exosome-mediated lnc-ABCA12-3 promotes proliferation and glycolysis but inhibits apoptosis by regulating the toll-like receptor 4/nuclear factor kappa-B signaling pathway in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a kind of malignant tumor with high incidence and mortality in the digestive system. The aim of this study is to explore the function of lnc-ABCA12-3 in the development of ESCC and its unique mechanisms. RT-PCR was applied to detect gene transcription levels in tissues or cell lines like TE-1, EC9706, and HEEC cells. Western blot was conducted to identify protein expression levels of mitochondrial apoptosis and toll-like receptor 4 (TLR4)/nuclear factor kappa-B (NF-κB) signaling pathway. CCK-8 and EdU assays were carried out to measure cell proliferation, and cell apoptosis was examined by flow cytometry. ELISA was used for checking the changes in glycolysis-related indicators. Lnc-ABCA12-3 was highly expressed in ESCC tissues and cells, which preferred it to be a candidate target. The TE-1 and EC9706 cells proliferation and glycolysis were obviously inhibited with the downregulation of lnc-ABCA12-3, while apoptosis was promoted. TLR4 activator could largely reverse the apoptosis acceleration and relieved the proliferation and glycolysis suppression caused by lnc-ABCA12-3 downregulation. Moreover, the effect of lnc-ABCA12-3 on ESCC cells was actualized by activating the TLR4/NF-κB signaling pathway under the mediation of exosome. Taken together, the lnc-ABCA12-3 could promote the proliferation and glycolysis of ESCC, while repressing its apoptosis probably by regulating the TLR4/NF-κB signaling pathway under the mediation of exosome.

The global decline in the sensitivity of vegetation productivity to precipitation from 2001 to 2018.

The sensitivity of vegetation productivity to precipitation (Sppt ) is a key metric for understanding the variations in vegetation productivity under changing precipitation and predicting future changes in ecosystem functions. However, a comprehensive assessment of Sppt over all the global land is lacking. Here, we investigated spatial patterns and temporal changes of Sppt across the global land from 2001 to 2018 with multiple streams of satellite observations. We found consistent spatial patterns of Sppt with different satellite products: Sppt was highest in dry regions while low in humid regions. Grassland and shrubland showed the highest Sppt , and evergreen needle-leaf forest and wetland showed the lowest. Temporally, Sppt showed a generally declining trend over the past two decades (p < .05), yet with clear spatial heterogeneities. The decline in Sppt was especially noticeable in North America and Europe, likely due to the increase in precipitation. In central Russia and Australia, however, Sppt showed an increasing trend. Biome-wise, most ecosystem types exhibited significant decrease in Sppt , while grassland, evergreen broadleaf forest, and mixed forest showed slight increases or non-significant changes in Sppt . Our finding of the overall decline in Sppt implies a potential stabilization mechanism for ecosystem productivity under climate change. However, the revealed Sppt increase for some regions and ecosystem types, in particular global grasslands, suggests that grasslands might be increasingly vulnerable to climatic variability with continuing global climate change.

Large-scale reforestation can increase water yield and reduce drought risk for water-insecure regions in the Asia-Pacific.

Large-scale reforestation can potentially bring both benefits and risks to the water cycle, which needs to be better quantified under future climates to inform reforestation decisions. We identified 477 water-insecure basins worldwide accounting for 44.6% (380.2 Mha) of the global reforestation potential. As many of these basins are in the Asia-Pacific, we used regional coupled land-climate modeling for the period 2041-2070 to reveal that reforestation increases evapotranspiration and precipitation for most water-insecure regions over the Asia-Pacific. This resulted in a statistically significant increase in water yield (p < .05) for the Loess Plateau-North China Plain, Yangtze Plain, Southeast China, and Irrawaddy regions. Precipitation feedback was influenced by the degree of initial moisture limitation affecting soil moisture response and thus evapotranspiration, as well as precipitation advection from other reforested regions and moisture transport away from the local region. Reforestation also reduces the probability of extremely dry months in most of the water-insecure regions. However, some regions experience nonsignificant declines in net water yield due to heightened evapotranspiration outstripping increases in precipitation, or declines in soil moisture and advected precipitation.

Role of Organic and Conservation Agriculture in Ammonia Emissions and Crop Productivity in China.

There is an increasing food demand with growing population and limited land for agriculture. Conventional agriculture with nitrogen (N) fertilizer applications, however, is a key source of ammonia (NH3) emissions that cause severe haze pollution and impair human health. Organic and conservation agricultural (OCA) practices are thereby recommended to address these dual challenges; however, whether OCA provides cobenefits for both air quality and crop productivity is controversial. Here, we perform a meta-analysis and machine learning algorithm with data from China, a global hotspot for agricultural NH3 emissions, to quantify the effects of OCA on NH3 emissions, crop yields and nitrogen use efficiency (NUE). We find that the effects of OCA depend on soil and climate conditions, and the 40-60% substitution of synthetic fertilizers with livestock manure achieves the maximum cobenefits of enhanced crop production and reduced NH3 emissions. Model forecasts further suggest that the appropriate application of livestock manure, straw return, and no-till could increase grain production up to 59.7 million metric tons (100% of straw return) and reduce maximum US$2.7 billion (60% substitution with livestock manure) in damage costs to human health from NH3 emissions by 2030. Our findings provide data-driven pathways and options for achieving multiple sustainable development goals and improving food systems and air quality in China.

Modification of lysine deacetylation regulates curcumol-induced necroptosis through autophagy in hepatic stellate cells.

The excessive deposition of extracellular matrix (ECM) is the main characteristic of liver fibrosis, and hepatic stellate cells (HSCs) are the main source of ECM. The removal of activated HSCs has a reversal effect on liver fibrosis. Western blot and MTT analysis indicated that curcumol could relieve hepatic fibrosis by promoting HSCs receptor-interacting protein kinase 1/3 (RIP1/RIP3)-dependent necroptosis. Importantly, autophagy flow was monitored by constructing the mRFP-GFP-LC3 plasmid, and it was found that curcumol cleared activated HSCs in a necroptosis manner that was dependent on autophagy. Our study suggested that the activation of necrosome formed by RIP1 and RIP3 depended on Atg5, and that autophagosomes were also necessary for curcumol-induced necroptosis. Furthermore, microscale thermophoresis and co-immunoprecipitation assay results proved that curcumol could target Sirt1 to regulate autophagy by reducing the acetylation level of Atg5. The HSCs-specific silencing of Sirt1 exacerbated CCl4 -induced liver fibrosis in mice. The deacetylation of Atg5 not only accelerated the accumulation of autophagosomes but also enhanced the interaction between Atg5 and RIP1/RIP3 to induce necroptosis. Overall, our study indicated that curcumol could activate Sirt1 to promote Atg5 deacetylation and enhanced its protein-protein interaction function, thereby inducing autophagy and promoting the necroptosis of HSCs to reduce liver fibrosis.

PPARα agonist fenofibrate attenuates iron-induced liver injury in mice by modulating the Sirt3 and ß-catenin signaling.

Iron accumulation is frequently associated with chronic liver diseases. However, our knowledge on how iron contributes to the liver injury is limited. Aberrant Wnt/ß-catenin signaling is a hallmark of several hepatic pathologies. We recently reported that peroxisome proliferator-activated receptor α (PPARα) agonist, fenofibrate, prevents iron-induced oxidative stress and ß-catenin signaling by chelating the iron. Sirtuin3 (Sirt3), a type of NAD+-dependent deacetylase, that plays a critical role in metabolic regulation was found to prevent ischemia reperfusion injury (IRI) by normalizing the Wnt/ß-catenin pathway. In the present study, we explored if fenofibrate prevents iron-induced liver injury by regulating the Sirt3 and ß-catenin signaling. In vitro and in vivo iron treatment resulted in the downregulation of PPARα, Sirt3, active ß-catenin, and its downstream target gene c-Myc in the mouse liver. Pharmacological activation of Sirt3, both in vitro and in vivo, by Honokiol (HK), a known activator of Sirt3, abrogated the inhibitory effect of iron overload on active ß-catenin expression and prevented the iron-induced upregulation of α smooth muscle actin (αSMA) and TGFß expression. Intrinsically, PPARα knockout mice showed significant downregulation of hepatic Sirt3 levels. In addition, treatment of iron overload mice with PPARα agonist fenofibrate reduced hepatic iron accumulation and prevented iron-induced downregulation of liver Sirt3 and active ß-catenin, mitigating the progression of fibrosis. Thus, our results establish a novel link between hepatic iron and PPARα, Sirt3, and ß-catenin signaling. Further exploration on the mechanisms by which fenofibrate ameliorates iron-induced liver injury likely has significant therapeutic impact on iron-associated chronic liver diseases.NEW & NOTEWORTHY Hepatic intracellular iron accumulation has been implicated in the pathophysiology of chronic liver diseases. In this study, we identified a novel mechanism involved in the progression of fibrosis. Excess iron accumulation in liver caused downregulation of PPARα-Sirt3-Wnt signaling leading to fibrosis. This work has significant translational potential as PPARα agonist fenofibrate could be an attractive therapeutic drug for the treatment of liver disorders associated with iron overload.

The mechanism research on the anti-liver fibrosis of emodin based on network pharmacology.

AIMS: This study was designated to illustrate the underlying mechanisms of emodin anti-liver fibrosis via network pharmacology and experiment. METHODS: The TSMCP and Genecards database were applied to screen the relevant targets of emodin or liver fibrosis. The essential target was selected by using Cytoscape to analyze the topological network of potential targets. Furthermore, we constructed a preliminary molecule docking study to explore the binding site by Surflex-Dock suite SYBYL X 2.0. The DAVID database was selected for gene functional annotations and KEGG enrichment analysis. Moreover, we demonstrated the ameliorating effect of emodin on carbon tetrachloride (CCl4 )-induced liver injury in mice. We also verified the network predictions in vitro via various techniques. RESULTS: The collected results showed that 35 targets were related to emodin, and 6,198 targets were associated with liver fibrosis. The Venn analysis revealed that 17 intersection targets were correlated with emodin anti-liver fibrosis. The topological network analysis suggested that the p53 was the remarkable crucial target. Besides, the molecule docking results showed that emodin could directly interact with p53 by binding the active site residues ASN345, GLN331, and TYR347. Finally, KEGG pathway enrichment results indicated that essential genes were mainly enriched in mitogen-activated protein kinase (MAPK) signaling pathways. Moreover, our study confirmed that emodin alleviated CCl4 -induced liver injury in mice, inducing hepatic stellate cells (HSCs) apoptosis via regulating the p53/ERK/p38 axis. CONCLUSIONS: This study partially verified the network pharmacological prediction of emodin inducing HSCs cell apoptosis through the p53/ERK/p38 axis.

Optimal temperature of vegetation productivity and its linkage with climate and elevation on the Tibetan Plateau.

Vegetation productivity first increases and then decreases with temperature; and temperature corresponding to the maximum productivity is called optimal temperature (Topt ). In this study, we used satellite derived near-infrared reflectance of vegetation (NIRv ) data to map Topt of vegetation productivity at the spatial resolution of 0.1° on the Tibetan Plateau (TP), one of most sensitive regions in the climate system. The average Topt of non-forest vegetation on the TP is about 14.7°C, significantly lower than the Topt value used in current ecosystem models. A remarkable geographical heterogeneity in Topt is observed over the TP. Higher Topt values generally appear in the north-eastern TP, while the south-western TP has relatively lower Topt (<10°C), in line with the difference of climate conditions and topography across different regions. Spatially, Topt tends to decrease by 0.41°C per 100 m increase in elevation, faster than the elevational elapse rate of growing season temperature, implying a potential CO2 regulation of Topt in addition to temperature acclimation. Topt increases by 0.66°C for each 1°C of rising mean annual temperature as a result of vegetation acclimation to climate change. However, at least at the decadal scale, there is no significant change in Topt between 2000s and 2010s, suggesting that the Topt climate acclimation may not keep up with the warming rate. Finally, future (2091-2100) warming could be close to and even surpass Topt on the TP under different RCP scenarios without considering potential climate acclimation. Our analyses imply that the temperature tipping point when the impact of future warming shifts from positive to negative on the TP is greatly overestimated by current vegetation models. Future research needs to include varying thermal and CO2 acclimation effects on Topt across different time scales in vegetation models.

Data-driven estimates of global litter production imply slower vegetation carbon turnover.

Accurate quantification of vegetation carbon turnover time (τveg ) is critical for reducing uncertainties in terrestrial vegetation response to future climate change. However, in the absence of global information of litter production, τveg could only be estimated based on net primary productivity under the steady-state assumption. Here, we applied a machine-learning approach to derive a global dataset of litter production by linking 2401 field observations and global environmental drivers. Results suggested that the observation-based estimate of global natural ecosystem litter production was 44.3 ± 0.4 Pg C year-1 . By contrast, land-surface models (LSMs) overestimated the global litter production by about 27%. With this new global litter production dataset, we estimated global τveg (mean value 10.3 ± 1.4 years) and its spatial distribution. Compared to our observation-based τveg , modelled τveg tended to underestimate τveg at high latitudes. Our empirically derived gridded datasets of litter production and τveg will help constrain global vegetation models and improve the prediction of global carbon cycle.

Seasonal changes in GPP/SIF ratios and their climatic determinants across the Northern Hemisphere.

Satellite-derived sun-induced chlorophyll fluorescence (SIF) has been increasingly used for estimating gross primary production (GPP). However, the relationship between SIF and GPP has not been well defined, impeding the translation of satellite observed SIF to GPP. Previous studies have generally assumed a linear relationship between SIF and GPP at daily and longer time scales, but support for this assumption is lacking. Here, we used the GPP/SIF ratio to investigate seasonal variations in the relationship between SIF and GPP over the Northern Hemisphere (NH). Based on multiple SIF products and MODIS and FLUXCOM GPP data, we found strong seasonal hump-shaped patterns for the GPP/SIF ratio over northern latitudes, with higher values in the summer than in the spring or autumn. This hump-shaped GPP/SIF seasonal variation was confirmed by examining different SIF products and was evident for most vegetation types except evergreen broadleaf forests. The seasonal amplitude of the GPP/SIF ratio decreased from the boreal/arctic region to drylands and the tropics. For most of the NH, the lowest GPP/SIF values occurred in October or September, while the maximum GPP/SIF values were evident in June and July. The most pronounced seasonal amplitude of GPP/SIF occurred in intermediate temperature and precipitation ranges. GPP/SIF was positively related to temperature in the early and late parts of the growing season, but not during the peak growing months. These shifting relationships between temperature and GPP/SIF across different months appeared to play a key role in the seasonal dynamics of GPP/SIF. Several mechanisms may explain the patterns we observed, and future research encompassing a broad range of climate and vegetation settings is needed to improve our understanding of the spatial and temporal relationships between SIF and GPP. Nonetheless, the strong seasonal variation in GPP/SIF we identified highlights the importance of incorporating this behavior into SIF-based GPP estimations.

Moisture availability mediates the relationship between terrestrial gross primary production and solar-induced chlorophyll fluorescence: Insights from global-scale variations.

Effective use of solar-induced chlorophyll fluorescence (SIF) to estimate and monitor gross primary production (GPP) in terrestrial ecosystems requires a comprehensive understanding and quantification of the relationship between SIF and GPP. To date, this understanding is incomplete and somewhat controversial in the literature. Here we derived the GPP/SIF ratio from multiple data sources as a diagnostic metric to explore its global-scale patterns of spatial variation and potential climatic dependence. We found that the growing season GPP/SIF ratio varied substantially across global land surfaces, with the highest ratios consistently found in boreal regions. Spatial variation in GPP/SIF was strongly modulated by climate variables. The most striking pattern was a consistent decrease in GPP/SIF from cold-and-wet climates to hot-and-dry climates. We propose that the reduction in GPP/SIF with decreasing moisture availability may be related to stomatal responses to aridity. Furthermore, we show that GPP/SIF can be empirically modeled from climate variables using a machine learning (random forest) framework, which can improve the modeling of ecosystem production and quantify its uncertainty in global terrestrial biosphere models. Our results point to the need for targeted field and experimental studies to better understand the patterns observed and to improve the modeling of the relationship between SIF and GPP over broad scales.

Unusual characteristics of the carbon cycle during the 2015-2016 El Niño.

The 2015-2016 El Niño was one of the strongest on record, but its influence on the carbon balance is less clear. Using Northern Hemisphere atmospheric CO2 observations, we found both detrended atmospheric CO2 growth rate (CGR) and CO2 seasonal-cycle amplitude (SCA) of 2015-2016 were much higher than that of other El Niño events. The simultaneous high CGR and SCA were unusual, because our analysis of long-term CO2 observations at Mauna Loa revealed a significantly negative correlation between CGR and SCA. Atmospheric inversions and terrestrial ecosystem models indicate strong northern land carbon uptake during spring but substantially reduced carbon uptake (or high emissions) during early autumn, which amplified SCA but also resulted in a small anomaly in annual carbon uptake of northern ecosystems in 2015-2016. This negative ecosystem carbon uptake anomaly in early autumn was primarily due to soil water deficits and more litter decomposition caused by enhanced spring productivity. Our study demonstrates a decoupling between seasonality and annual carbon cycle balance in northern ecosystems over 2015-2016, which is unprecedented in the past five decades of El Niño events.

A novel lncRNA PLK4 up-regulated by talazoparib represses hepatocellular carcinoma progression by promoting YAP-mediated cell senescence.

A growing number of studies recognize that long non-coding RNAs (lncRNAs) are essential to mediate multiple tumorigenic processes, including hepatic tumorigenesis. However, the pathological mechanism of lncRNA-regulated liver cancer cell growth remains poorly understood. In this study, we identified a novel function lncRNA, named polo-like kinase 4 associated lncRNA (lncRNA PLK4, GenBank Accession No. RP11-50D9.3), whose expression was dramatically down-regulated in hepatocellular carcinoma (HCC) tissues and cells. Interestingly, talazoparib, a novel and highly potent poly-ADP-ribose polymerase 1/2 (PARP1/2) inhibitor, could increase lncRNA PLK4 expression in HepG2 cells. Importantly, we showed that talazoparib-induced lncRNA PLK4 could function as a tumour suppressor gene by Yes-associated protein (YAP) inactivation and induction of cellular senescence to inhibit liver cancer cell viability and growth. In summary, our findings reveal the molecular mechanism of talazoparib-induced anti-tumor effect, and suggest a potential clinical use of talazoparib-targeted lncRNA PLK4/YAP-dependent cellular senescence for the treatment of HCC.

The 400-year natural history of a tropical coastal mangrove-fringed lagoon: What can we learn?

Coastal lagoon ecosystems are important natural carbon sinks but are threatened by both climate change and direct human disturbances. By reconstructing the environmental change and carbon dynamics of the past 400 years for a tropical mangrove-fringed lagoon system in Java, Indonesia, Hapsari et al. provide important insights into how climate change and human land use co-regulate the vegetation and carbon cycles of a tropical lagoon system and its catchment. This article is a commentary on Hapsari et al. 26, 1414-1431.