Emergent constraints on historical and future global gross primary productivity.

Terrestrial gross primary productivity (GPP) is the largest carbon flux in the global carbon cycle and plays a crucial role in terrestrial carbon sequestration. However, historical and future global GPP estimates still vary markedly. In this study, we reduced uncertainties in global GPP estimates by employing an innovative emergent constraint method on remote sensing-based GPP datasets (RS-GPP), using ground-based estimates of GPP from flux towers as the observational constraint. Using this approach, the global GPP in 2001-2014 was estimated to be 126.8 ± 6.4 PgC year-1, compared to the original RS-GPP ensemble mean of 120.9 ± 10.6 PgC year-1, which reduced the uncertainty range by 39.6%. Independent space- and time-based (different latitudinal zones, different vegetation types, and individual year) constraints further confirmed the robustness of the global GPP estimate. Building on these insights, we extended our constraints to project global GPP estimates in 2081-2100 under various Shared Socioeconomic Pathway (SSP) scenarios: SSP126 (140.6 ± 9.3 PgC year-1), SSP245 (153.5 ± 13.4 PgC year-1), SSP370 (170.7 ± 16.9 PgC year-1), and SSP585 (194.1 ± 23.2 PgC year-1). These findings have important implications for understanding and projecting climate change, helping to develop more effective climate policies and carbon reduction strategies.

A multi-model approach to assessing the impacts of catchment characteristics on spatial water quality in the Great Barrier Reef catchments.

Water quality monitoring programs often collect large amounts of data with limited attention given to the assessment of the dominant drivers of spatial and temporal water quality variations at the catchment scale. This study uses a multi-model approach: a) to identify the influential catchment characteristics affecting spatial variability in water quality; and b) to predict spatial variability in water quality more reliably and robustly. Tropical catchments in the Great Barrier Reef (GBR) area, Australia, were used as a case study. We developed statistical models using 58 catchment characteristics to predict the spatial variability in water quality in 32 GBR catchments. An exhaustive search method coupled with multi-model inference approaches were used to identify important catchment characteristics and predict the spatial variation in water quality across catchments. Bootstrapping and cross-validation approaches were used to assess the uncertainty in identified important factors and robustness of multi-model structure, respectively. The results indicate that water quality variables were generally most influenced by the natural characteristics of catchments (e.g., soil type and annual rainfall), while anthropogenic characteristics (i.e., land use) also showed significant influence on dissolved nutrient species (e.g., NOX, NH4 and FRP). The multi-model structures developed in this work were able to predict average event-mean concentration well, with Nash-Sutcliffe coefficient ranging from 0.68 to 0.96. This work provides data-driven evidence for catchment managers, which can help them develop effective water quality management strategies.

Blockade of AIM2 inflammasome or α1-AR ameliorates IL-1ß release and macrophage-mediated immunosuppression induced by CAR-T treatment.

BACKGROUND: Interleukin (IL) 1 released from monocytes/macrophages is one of the critical determinants in mediating the adverse events of chimeric antigen receptor T cell (CAR-T) therapy, including cytokine release syndrome and neurotoxicity. However, the molecular mechanisms of IL-1 production during CAR-T therapy remain unknown. METHODS: The roles of AIM2 and α1-adrenergic receptor (α1-AR) in CAR-T treatment-induced IL-1ß release were evaluated by gene silencing, agonist or antagonist treatment. The phenotype switch of macrophages in response to CAR-T treatment was analyzed concerning cytotoxicity of CAR-T cells and proliferation of activated T cells. RESULTS: This study provided the experimental evidence that CAR-T treatment-induced activation of AIM2 inflammasome of macrophages resulted in the release of bioactive IL-1ß. CAR-T treatment-induced α1-AR-mediated adrenergic signaling augmented the priming of AIM2 inflammasome by enhancing IL-1ß mRNA and AIM2 expression. Meanwhile, tumor cell DNA release triggered by CAR-T treatment potentiated the activation of AIM2 inflammasome in macrophages. Interestingly, an apparent phenotypic switch in macrophages occurred after interacting with CAR-T/tumor cells, which greatly inhibited the cytotoxicity of CAR-T cells and proliferation of activated T cells through upregulation of programmed cell death-ligand 1 (PD-L1) and indoleamine 2,3-dioxygenase (IDO) in the macrophages. Blockade of AIM2 inflammasome or α1-AR reversed the upregulation of PD-L1 and IDO and the phenotypic switch of the macrophages. CONCLUSION: Our study implicates that CAR-T therapy combined with the blockade of AIM2 inflammasome or α1-AR may relieve IL-1ß-related toxic side effects of CAR-T therapy and ensure antitumor effects of the treatment.

ß2-AR activation promotes cleavage and nuclear translocation of Her2 and metastatic potential of cancer cells.

Prolonged hypersecretion of catecholamine induced by chronic stress may correlate with malignant progression of cancer. ß2-adrenergic receptor (ß2-AR) overexpressed in certain cancer cells may translate the signals from neuroendocrine system to malignant signals by interacting with oncoproteins, such as Her2. In the present study, we demonstrate that catecholamine stimulation activates the expression and proteolytic activity of ADAM10 by modulating the expression of miR-199a-5p and SIRT1 and also confirm that catecholamine induction triggers the activities of γ-secretase, leading to shedding of Her2 extracellular domain (ECD) by ADAM10 and subsequent intramembranous cleavage of Her2 intracellular domain (ICD) by presenilin-dependent γ-secretase, nuclear translocation of Her2 ICD, and enhanced transcription of tumor metastasis-associated gene COX-2. Chronic stimulation of catecholamine strongly promotes the invasive activities of cancer cells in vitro and spontaneous tumor lung metastasis in mice. Furthermore, nuclear localization of Her2 was significantly correlated with overexpression of ß2-AR in human breast cancer tissues, indicating that catecholamine-induced ß2-AR activation plays decisive roles in tumor metastasis. Our data also reveal that an unknown mechanism by which the regulated intramembrane proteolysis (RIP) initiated by ß2-AR-mediated signaling controls a novel Her2-mediated signaling transduction.

A simulation-based approach to assess the power of trend detection in high- and low-frequency water quality records.

To provide more precise understanding of water quality changes, continuous sampling is being used more in surface water quality monitoring networks. However, it remains unclear how much improvement continuous monitoring provides over spot sampling, in identifying water quality changes over time. This study aims (1) to assess our ability to detect trends using water quality data of both high and low frequencies and (2) to assess the value of using high-frequency data as a surrogate to help detect trends in other constituents. Statistical regression models were used to identify temporal trends and then to assess the trend detection power of high-frequency (15 min) and low-frequency (monthly) data for turbidity and electrical conductivity (EC) data collected across Victoria, Australia. In addition, we developed surrogate models to simulate five sediment and nutrients constituents from runoff, turbidity and EC. A simulation-based statistical approach was then used to the compare the power to detect trends between the low- and high-frequency water quality records. Results show that high-frequency sampling shows clear benefits in trend detection power for turbidity, EC, as well as simulated sediment and nutrients, especially over short data periods. For detecting a 1% annual trend with 5 years of data, up to 97% and 94% improvements on the trend detection probability are offered by high-frequency data compared with monthly data, for turbidity and EC, respectively. Our results highlight the benefits of upgrading monitoring networks with wider application of high-frequency sampling.

CRISPR screen in mechanism and target discovery for cancer immunotherapy.

CRISPR/Cas-based genetic perturbation screens have emerged as powerful tools for large-scale identification of new targets for cancer immunotherapy. Various strategies of CRISPR screen have been used for immune-oncology (IO) target discovery. The genomic sequences targeted by CRISPR/Cas system range from coding sequences to non-coding RNA/DNA, including miRNAs, LncRNAs, circRNAs, promoters, and enhancers, which may be potential targets for future pharmacological and therapeutic interventions. Rapid progresses have been witnessed in finding novel targets for enhancing tumor antigen presentation, sensitizing of tumor cells to immune-mediated cytotoxicity, and reinvigorating tumor-specific T cells by using CRISPR technologies. In combination with other strategies, the detailed characteristics of the targets for immunotherapy have been obtained by CRISPR screen. In this review, we present an overview of recent progresses in the development of CRISPR-based screens for IO target identification and discuss the challenges and possible solutions in this rapidly growing field.