SpTNF regulates apoptosis and antimicrobial peptide synthesis in mud crab (Scylla paramamosain) during white spot syndrome virus infection.

Tumor necrosis factor (TNF) is an inflammatory cytokine that is important in cell survival, proliferation, differentiation, and death. However, the functions of TNF in the innate immune responses of invertebrates have been less studied. In this study, SpTNF was cloned and characterized from mud crab (Scylla paramamosain) for the first time. SpTNF contains an open reading frame of 354 bp encoding 117 deduced amino acids, with a conserved C-terminal TNF homology domain (THD) domain. RNAi knockdown of SpTNF reduced hemocyte apoptosis and antimicrobial peptide (AMP) synthesis. Expression of SpTNF was initially down-regulated but subsequently up-regulated after 48 h in hemocytes of mud crabs after WSSV infection. Results of RNAi knockdown and overexpression showed that SpTNF inhibits the WSSV infection through activating apoptosis, NF-κB pathway, and AMP synthesis. Furthermore, the lipopolysaccharide-induced TNF-α factor (SpLITAF) can regulate the expression of SpTNF, induction of apoptosis, and activation of the NF-κB pathway and AMP synthesis. The expression and nuclear translocation of SpLITAF were found to be regulated by WSSV infection. Knocking down of SpLITAF increased the WSSV copy number and expression of VP28 gene. Taken together, these results proved the protective function of SpTNF, which is regulated by SpLITAF, in the immune response of mud crabs against WSSV through the regulation of apoptosis and activation of AMP synthesis.

Engineering Climate-Resilient Rice Using a Nanobiostimulant-Based "Stress Training" Strategy.

Under a changing climate, cultivating climate-resilient crops will be critical to maintaining food security. Here, we propose the application of reactive oxygen species (ROS)-generating nanoparticles as nanobiostimulants to trigger stress/immune responses and subsequently increase the stress resilience of plants. We established three regimens of silver nanoparticles (AgNPs)-based "stress training": seed training (ST), leaf training (LT), and combined seed and leaf training (SLT). Trained rice seedlings were then exposed to either rice blast fungus (Magnaporthe oryzae) or chilling stress (10 °C). The results show that all "stress training" regimes, particularly SLT, significantly enhanced the resistance of rice against the fungal pathogen (lesion size reduced by 82% relative to untrained control). SLT also significantly enhanced rice tolerance to cold stress. The mechanisms for the enhanced resilience were investigated with metabolomics and transcriptomics, which show that "stress training" induced considerable metabolic and transcriptional reprogramming in rice leaves. AgNPs boosted ROS-activated stress signaling pathways by oxidative post-translational modifications of stress-related kinases, hormones, and transcriptional factors (TFs). These signaling pathways subsequently modulated the expression of defense genes, including specialized metabolites (SMs) biosynthesis genes, cell membrane lipid metabolism genes, and pathogen-plant interaction genes. Importantly, results showed that the "stress memory" can be transferred transgenerationally, conferring offspring seeds with improved seed germination and seedling vigor. This may provide an epigenetic breeding strategy to fortify stress resilience of crops. This nanobiostimulant-based stress training strategy will increase yield vigor against a changing climate and will contribute to sustainable agriculture by reducing agrochemical use.

Geomorphic response of outburst floods: Insight from numerical simulations and observations--The 2018 Baige outburst flood in the upper Yangtze River.

Outburst floods related to glacial or landslide damming are a major agent of geomorphic change in mountain rivers. Although the evidence between outburst flooding and riverine landscapes has been gradually recognized, the lack of hydraulics to the extent that there has still not been quantified on the relationship of how the amount and spatial distribution of these changes relate quantitatively to the hydraulic conditions and durations of these catastrophic events. This study combined remote and field observations of the 2018 Baige outburst flood with two-dimensional numerical simulation using the diffusive wave equation. By feeding the measured dam-breach hydrograph and comparing three different Manning coefficients in numerical experiments, the simulation results show that when n = 0.055, the time of peak flow was only 0.5 h different from that indicated by measured data in Yebatan, 54 km downstream of the Baige landslide dam. Under high shear stress over several hours at sustained ~20 m water depth, lateral erosion caused by these outburst floods contributed to the adjacent landslide, which was activated in association with intermittent water velocity waves of approximately 17 m/s. Sustained high stream power (>50 kW m2) from the outburst flood eroded slope toes and accelerated slippage of six slopes. Combining simulation and observations, we also developed a physical model related to hillslope instability caused by high hydrodynamic erosion of riverbanks generated by flow waves lasting several hours, which explained the hydrodynamic response of the outburst flood to the canyon geomorphology. Furthermore, we suggest that the pattern of channel widening erosion and deposition is governed by the variation in shear stress and Froude number as the high-energy flood flows from a wide channel into a narrow river valley. Our findings highlight that the hydraulics of high-magnitude outburst floods and sediment transport play crucial roles in reshaping canyon geomorphology.

SpBNIP3 regulates apoptosis and autophagy in mud crab (Scylla paramamosain) during white spot syndrome virus infection.

BNIP3 (BCL2 and adenovirus E1B 19-kDa-interacting protein 3), which is a pro-apoptotic protein in the BCL-2 family involves a variety of cell signaling pathways, including mitochondrial dysfunction, mitochondrial autophagy, and apoptosis in vertebrates. However, the role of BNIP3 in the regulation of apoptosis and/or autophagy in crustaceans suffering virus infection is still limited. In this study, the mud crab (Scylla paramamosain) BNIP3 (SpBNIP3) was identified and studied to elucidate its association with the white spot syndrome virus (WSSV) infection. SpBNIP3 was widely expressed in all tested tissues and significantly down-regulated in the hemocytes of mud crab after WSSV infection. Knockdown of SpBNIP3 using RNA interference increased the apoptosis rate and Caspase 3 activity but decreased the mitochondrial membrane potential and autophagy levels, as well as viral copy number in mud crabs infected with WSSV. Additionally, the relationship between the viral infection and the autophagy of hemocytes was observed. The level of autophagy was reduced upon WSSV infection, and the activation of autophagy enriched the viral copy number. Taken together, the results of this study provide a new finding on the mechanism that SpBNIP3 may participate in the WSSV infection through the regulation of apoptosis and autophagy processes in mud crabs.

A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies from 1992 to 2020.

Distinguishing gross primary production of sunlit and shaded leaves (GPPsun and GPPshade) is crucial for improving our understanding of the underlying mechanisms regulating long-term GPP variations. Here we produce a global 0.05°, 8-day dataset for GPP, GPPshade and GPPsun over 1992-2020 using an updated two-leaf light use efficiency model (TL-LUE), which is driven by the GLOBMAP leaf area index, CRUJRA meteorology, and ESA-CCI land cover. Our products estimate the mean annual totals of global GPP, GPPsun, and GPPshade over 1992-2020 at 125.0 ± 3.8 (mean ± std) Pg C a-1, 50.5 ± 1.2 Pg C a-1, and 74.5 ± 2.6 Pg C a-1, respectively, in which EBF (evergreen broadleaf forest) and CRO (crops) contribute more than half of the totals. They show clear increasing trends over time, in which the trend of GPP (also GPPsun and GPPshade) for CRO is distinctively greatest, and that for DBF (deciduous broadleaf forest) is relatively large and GPPshade overwhelmingly outweighs GPPsun. This new dataset advances our in-depth understanding of large-scale carbon cycle processes and dynamics.

Improved estimation of global gross primary productivity during 1981-2020 using the optimized P model.

Accurate estimation of terrestrial gross primary productivity (GPP) is essential for quantifying the net carbon exchange between the atmosphere and biosphere. Light use efficiency (LUE) models are widely used to estimate GPP at different spatial scales. However, difficulties in proper determination of maximum LUE (LUEmax) and downregulation of LUEmax into actual LUE result in uncertainties in GPP estimated by LUE models. The recently developed P model, as a LUE-like model, captures the deep mechanism of photosynthesis and simplifies parameterization. Site level studies have proved the outperformance of P model over LUE models. However, the global application of the P model is still lacking. Thus, the effectiveness of 5 water stress factors integrated into the P model was compared. The optimal P model was used to generate a new long-term (1981-2020) global monthly GPP dataset at a spatial resolution of 0.1° × 0.1°, called PGPP. Validation at globally distributed 109 FLUXNET sites indicated that PGPP is better than three widely-used GPP products. R2 between PGPP and observed GPP equals to 0.75, the corresponding root mean squared error (RMSE) and mean absolute error (MAE) equal to 1.77 g C m-2 d-1 and 1.28 g C m-2 d-1. During the period from 1981 to 2020, PGPP significantly increased in 69.02% of global vegetated regions (p < 0.05). Overall, PGPP provides a new GPP product choice for global ecology studies and the comparison of various water stress factors provides a new idea for the improvement of GPP model in the future.

Revisiting the cumulative effects of drought on global gross primary productivity based on new long-term series data (1982-2018).

Drought has broad and deep impacts on vegetation. Studies on the effects of drought on vegetation have been conducted over years. Recently, the cumulative effect of drought is recognized as another key factor affecting plant growth. However, global-scale studies on this phenomenon are still lacking. Thus, based on new satellite based gross primary productivity (GPP) and multi-temporal scale Standardized Precipitation Evapotranspiration Index data sets, we explored the cumulative effect duration (CED) of drought on global vegetation GPP and analyzed its variability across elevations and climatic zones. The main findings were as follows: (1) The cumulative effect of drought on GPP was widespread, with an average CED of 4.89 months. (2) CED of drought on GPP varied among vegetation types. Specifically, grasslands showed the longest duration, with an average value of 5.28 months, followed by shrublands (5.09 months), wetlands (5.03 months), croplands (4.85 months), savannas (4.58 months), and forestlands (4.57 months). (3) CED of drought on GPP changes with climate conditions. It decreased with the decrease of precipitation in the driest month (Pdry ) and mean annual precipitation in tropical and arid climate zones, respectively. In both temperate and cold climate zones, CED of drought on GPP was shorter in areas with dry winter than that in areas with dry summer. It increased with the decrease of mean annual air temperature in tropical climate zones and decreased with the increase of summer temperature in temperate and cold climatic zones. (4) With increasing elevation, CED of drought on GPP showed a pattern of increasing (0-3000 m), then decreasing (3000-5000 m), and increasing again (>5000 m). Our findings highlight the heterogeneity of CED of drought on GPP, owing to differences in vegetation types, long-term hydrothermal conditions, elevation, etc. The results could deepen our understanding of the effects of drought on global vegetation.

The effect of water stress on net primary productivity in northwest China.

Net primary productivity (NPP) has been widely used as the indicator of vegetation function and exhibits large spatial and temporal variations caused by numerous factors. Northwest China (NWC) is one of the driest regions in China, and water supply is the key determinant of NPP here. However, studies on the effects of water stress on NPP in NWC at the regional scale are still relatively lacking. Thus, in this study, based on a set of Moderate-Resolution Imaging Spectroradiometer (MODIS) NPP and evapotranspiration (ET) datasets, we quantified the response of NPP to water stress, which is indicated by crop water stress index (CWSI). Regional average of annual NPP in NWC showed an increasing trend during the study period, at a rate of 0.84 g C m-2 yr-1. At the province level, the NPP increase rates increased in the order of Ningxia (7.7%), Shaanxi (6.5%), Gansu (4.5%), Qinghai (3.8%), and Xinjiang (1.7%). NPP was negatively correlated with CWSI (p<0.05) in 73% of areas, indicating the key role of water stress in constraining NPP over this arid region. The effect of water stress on NPP changes with elevation. Water stress has the strongest negative impact on NPP in areas with elevations around 2000 m. In elevations above 5000 m, NPP is not limited by water stress, mostly positively correlated with CWSI. Our findings further clarify the importance of water stress in dryland ecosystems, while highlighting that elevation gradients can significantly affect the correlation between NPP and water stress.

SpATF2 participates in maintaining the homeostasis of hemolymph microbiota by regulating dual oxidase expression in mud crab.

Activating transcription factors 2 (ATF2) is a transcription factor of the members of ATF/CREB family that is phosphorylated and activated by the mitogen-activated protein kinase (MAPK) in responding to the stimulation of stimuli. In present study, SpATF2 from mud crab (Scylla paramamosain) was identified and studied. The open reading frame of SpATF2 with 2136 bp in length encodes a protein with 711 amino acids. The SpATF2 protein includes the putative zinc finger domain in the N-terminus and bZIP type DNA-binding domain in the C-terminal. Tissue distribution of SpATF2 transcripts showed that SpATF2 was ubiquitously expressed in all examined tissues of the untreated mud crabs, with the highest expression levels in muscle and hepatopancreas. The transcriptional level of SpATF2 was up-regulated in the hemocytes after Vibrio parahemolyticus or WSSV infection. Reporter gene assays indicated that SpATF2 could activate the expression of dual oxidase (SpDuox1) in S. paramamosain. The RNA interference (RNAi) of SpATF2 significantly decreased the expression of SpDuox1, and consequently reduced reactive oxygen species production thereby significantly increased the bacterial load in the hemolymph of mud crabs. Similarly, significant reduction in bacterial clearance of hemolymph was observed after the V. parahemolyticus infection in SpATF2 knockdown mud crabs. This study showed that SpATF2 played a vital role in maintaining homeostasis of the hemolymph microbiota through regulating the expression of dual oxidase of mud crab.

Remotely monitoring ecosystem respiration from various grasslands along a large-scale east-west transect across northern China.

BACKGROUND: Grassland ecosystems play an important role in the terrestrial carbon cycles through carbon emission by ecosystem respiration (Re) and carbon uptake by plant photosynthesis (GPP). Surprisingly, given Re occupies a large component of annual carbon balance, rather less attention has been paid to developing the estimates of Re compared to GPP. RESULTS: Based on 11 flux sites over the diverse grassland ecosystems in northern China, this study examined the amounts of carbon released by Re as well as the dominant environmental controls across temperate meadow steppe, typical steppe, desert steppe and alpine meadow, respectively. Multi-year mean Re revealed relatively less CO2 emitted by the desert steppe in comparison with other grassland ecosystems. Meanwhile, C emissions of all grasslands were mainly controlled by the growing period. Correlation analysis revealed that apart from air and soil temperature, soil water content exerted a strong effect on the variability in Re, which implied the great potential to derive Re using relevant remote sensing data. Then, these field-measured Re data were up-scaled to large areas using time-series MODIS information and remote sensing-based piecewise regression models. These semi-empirical models appeared to work well with a small margin of error (R2 and RMSE ranged from 0.45 to 0.88 and from 0.21 to 0.69 g C m-2 d-1, respectively). CONCLUSIONS: Generally, the piecewise models from the growth period and dormant season performed better than model developed directly from the entire year. Moreover, the biases between annual mean Re observations and the remotely-derived products were usually within 20%. Finally, the regional Re emissions across northern China's grasslands was approximately 100.66 Tg C in 2010, about 1/3 of carbon fixed from the MODIS GPP product. Specially, the desert steppe exhibited the highest ratio, followed by the temperate meadow steppe, typical steppe and alpine meadow. Therefore, this work provides a novel framework to accurately predict the spatio-temporal patterns of Re over large areas, which can greatly reduce the uncertainties in global carbon estimates and climate projections.

Performance of a two-leaf light use efficiency model for mapping gross primary productivity against remotely sensed sun-induced chlorophyll fluorescence data.

Estimating terrestrial gross primary production is an important task when studying the carbon cycle. In this study, the ability of a two-leaf light use efficiency model to simulate regional gross primary production in China was validated using satellite Global Ozone Monitoring Instrument - 2 sun-induced chlorophyll fluorescence data. The two-leaf light use efficiency model was used to estimate daily gross primary production in China's terrestrial ecosystems with 500-m resolution for the period from 2007 to 2014. Gross primary production simulated with the two-leaf light use efficiency model was resampled to a spatial resolution of 0.5° and then compared with sun-induced chlorophyll fluorescence. During the study period, sun-induced chlorophyll fluorescence and gross primary production simulated by the two-leaf light use efficiency model exhibited similar spatial and temporal patterns in China. The correlation coefficient between sun-induced chlorophyll fluorescence and monthly gross primary production simulated by the two-leaf light use efficiency model was significant (p<0.05, n=96) in 88.9% of vegetated areas in China (average value 0.78) and varied among vegetation types. The interannual variations in monthly sun-induced chlorophyll fluorescence and gross primary production simulated by the two-leaf light use efficiency model were similar in spring and autumn in most vegetated regions, but dissimilar in winter and summer. The spatial variability of sun-induced chlorophyll fluorescence and gross primary production simulated by the two-leaf light use efficiency model was similar in spring, summer, and autumn. The proportion of spatial variations of sun-induced chlorophyll fluorescence and annual gross primary production simulated by the two-leaf light use efficiency model explained by ranged from 0.76 (2011) to 0.80 (2013) during the study period. Overall, the two-leaf light use efficiency model was capable of capturing spatial and temporal variations in gross primary production in China. However, the model needs further improvement to better simulate gross primary production in summer.

Water use efficiency of China's terrestrial ecosystems and responses to drought.

Water use efficiency (WUE) measures the trade-off between carbon gain and water loss of terrestrial ecosystems, and better understanding its dynamics and controlling factors is essential for predicting ecosystem responses to climate change. We assessed the magnitude, spatial patterns, and trends of WUE of China's terrestrial ecosystems and its responses to drought using a process-based ecosystem model. During the period from 2000 to 2011, the national average annual WUE (net primary productivity (NPP)/evapotranspiration (ET)) of China was 0.79 g C kg(-1) H2O. Annual WUE decreased in the southern regions because of the decrease in NPP and the increase in ET and increased in most northern regions mainly because of the increase in NPP. Droughts usually increased annual WUE in Northeast China and central Inner Mongolia but decreased annual WUE in central China. "Turning-points" were observed for southern China where moderate and extreme droughts reduced annual WUE and severe drought slightly increased annual WUE. The cumulative lagged effect of drought on monthly WUE varied by region. Our findings have implications for ecosystem management and climate policy making. WUE is expected to continue to change under future climate change particularly as drought is projected to increase in both frequency and severity.

[Simulation of cropland soil moisture based on an ensemble Kalman filter].

By using an ensemble Kalman filter (EnKF) to assimilate the observed soil moisture data, the modified boreal ecosystem productivity simulator (BEPS) model was adopted to simulate the dynamics of soil moisture in winter wheat root zones at Xuzhou Agro-meteorological Station, Jiangsu Province of China during the growth seasons in 2000-2004. After the assimilation of observed data, the determination coefficient, root mean square error, and average absolute error of simulated soil moisture were in the ranges of 0.626-0.943, 0.018-0.042, and 0.021-0.041, respectively, with the simulation precision improved significantly, as compared with that before assimilation, indicating the applicability of data assimilation in improving the simulation of soil moisture. The experimental results at single point showed that the errors in the forcing data and observations and the frequency and soil depth of the assimilation of observed data all had obvious effects on the simulated soil moisture.

[Forest canopy leaf area index in Maoershan Mountain: ground measurement and remote sensing retrieval].

Leaf area index (LAI) is one of the most important structural parameters of terrestrial ecosystem, while the remote sensing retrieval and the ground optical instrument measurement and based on canopy gap model are the effective approaches to rapidly obtain LAI. However, these two approaches can only acquire effective LAI (LAI(e)), due to the clumping of vegetation canopy. Taking the experimental forest farm of Northeast Forestry University at Maoershan Mountain in Heilongjiang Province of Northeast China as study site, this paper measured the forest canopy LAI(e) by LAI2000, and estimated the LAI by the combination of TRAC (tracing radiation and architecture of canopies) measurement of foliage clumping index. A LAI remote sensing retrieval model was constructed through the analysis of the relationships between different vegetation indices calculated from Landsat5-TM and measured LAI(e). The results showed that at the study site, the LAI of broad leaved forests was close to the LAI(e), but the LAI of needle leaved forests was 27% larger than the LAI(e). Reduced simple ratio index (RSR) had the highest relationship with measured LAI(e) (R2 = 0.763, n = 23), which could be used as the best predictor of LAI. The LAI at study site increased rapidly with increasing elevation when the elevation was below 400 m, but had a slow increase when the elevation was from 400 m to 750 m. When the elevation was above 750 m, the LAI decreased. There was a significant correlation between the forest canopy LAI and aboveground biomass.