A dataset of CMIP6-based climate scenarios for climate change impact assessment in Great Britain.

Climate change is a critical issue in the 21st century. Assessment of the impacts of climate change is beneficial for assisting advanced recommendations for adaptations. Climate change impact assessments require high quality local-scale climate scenarios. The future climate projections from Global Climate Models (GCMs) are problematic to use at local scale due to their coarse spatial and temporal resolution, and existing biases. It is important to have climate change scenarios based on the GCMs ensemble downscaled to local scale to account for inherent uncertainty in climate projections, and to have a sufficient large number of years to account for inter-annual climate variability and low frequency, but high impact, extreme climatic events. A dataset of future climate change scenarios was therefore generated at 26 representative sites across Great Britain based on the latest CMIP6 multi-model ensemble downscaled to local-scale by using a stochastic weather generator, LARS-WG 8.0. The data set consists of climate scenarios of daily weather of 1,000 realizations of typical years for a baseline, and very near (2030) and near-future (2050) climates, based on five GCMs and two emission scenarios (Shared Socioeconomic Pathways - SSPs viz. SSP2-4.5 and SSP5-8.5). A total of 15 GCMs from the CMIP6 ensemble were integrated in LARS-WG 8.0. LARS-WG downscales future climate projections from the GCMs and incorporates changes at local scale in the mean climate, climatic variability, and extreme events by modifying the statistical distributions of the weather variables at each site. Based on the performance of the GCMs over northern Europe and their climate sensitivity, a subset of five GCMs was selected, viz.; ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR and MRI-ESM2-0. The selected GCMs are evenly distributed among the full set of 15 GCMs. The use of a subset of GCMs substantially reduces computational time, while allowing assessment of uncertainties in impact studies related to uncertain future climate projections arising from GCMs. The 1000 years of daily weather for the baseline, as well as for very near and near-future climate change scenarios, are essential for estimating inter-annual variation, and for detecting low frequency, but high impact, extreme climatic events, such as heat waves, floods and droughts. The present dataset can be used as an input to climate change impact models in various fields, including, land and water resources, agriculture and food production, ecology and epidemiology, and human health and welfare. Researchers, breeders, farm managers, social and public sector leaders, and policymakers may benefit from this new dataset when undertaking impact assessments of climate change and decision support for mitigation and adaptation to climate change.

Synergistic Effects of PARP Inhibition and Cholesterol Biosynthesis Pathway Modulation.

An in-depth multiomic molecular characterization of PARP inhibitors revealed a distinct poly-pharmacology of niraparib (Zejula) mediated by its interaction with lanosterol synthase (LSS), which is not observed with other PARP inhibitors. Niraparib, in a similar way to the LSS inhibitor Ro-48-8071, induced activation of the 24,25-epoxysterol shunt pathway, which is a regulatory signaling branch of the cholesterol biosynthesis pathway. Interestingly, the combination of an LSS inhibitor with a PARP inhibitor that does not bind to LSS, such as olaparib, had an additive effect on killing cancer cells to levels comparable with niraparib as a single agent. In addition, the combination of PARP inhibitors and statins, inhibitors of 3-hydroxy-3-methylglutaryl-coenzyme A reductase, an enzyme catalyzing the rate-limiting step in the mevalonate pathway, had a synergistic effect on tumor cell killing in cell lines and patient-derived ovarian tumor organoids. These observations suggest that concomitant inhibition of the cholesterol biosynthesis pathway and PARP activity might result in stronger efficacy of these inhibitors against tumor types highly dependent on cholesterol metabolism. SIGNIFICANCE: The presented data indicate, to our knowledge, for the first time, the potential benefit of concomitant modulation of cholesterol biosynthesis pathway and PARP inhibition and highlight the need for further investigation to assess its translational relevance.

PARP inhibitor synthetic lethality in ATM biallelic mutant cancer cell lines is associated with BRCA1/2 and RAD51 downregulation.

Background: Ataxia telangiectasia-mutated (ATM) kinase is a central regulator of the DNA damage response (DDR) signaling pathway, and its function is critical for the maintenance of genomic stability in cells that coordinate a network of cellular processes, including DNA replication, DNA repair, and cell cycle progression. ATM is frequently mutated in human cancers, and approximately 3% of lung cancers have biallelic mutations in ATM, i.e., including 3.5% of lung adenocarcinomas (LUAD) and 1.4% of lung squamous cell carcinomas (LUSC). Methods: We investigated the potential of targeting the DDR pathway in lung cancer as a potential therapeutic approach. In this context, we examined whether ATM loss is synthetically lethal with niraparib monotherapy. This exploration involved the use of hATM knockout (KO) isogenic cell lines containing hATM homozygous (-/-) and heterozygous (+/-) generated via CRISPR/Cas9 gene knockout technology in DLD-1, a human colorectal adenocarcinoma cell line. Subsequently, we extended our investigation to non-small cell lung cancer (NSCLC) patient derived xenograft (PDX) models for further validation of poly ADP-ribose polymerase inhibitor (PARPi) synthetic lethality in ATM mutant NSCLC models. Results: Here, we demonstared that biallelic hATM deletion (-/-) in DLD-1 impairs homologous recombination (HR) repair function and sensitizes cells to the PARPi, niraparib. Niraparib also caused significant tumor regression in one-third of the NSCLC PDX models harboring deleterious biallelic ATM mutations. Loss of hATM (-/-) was concomitantly associated with low BRCA1 and BRCA2 protein expression in both the hATM (-/-) DLD-1 cell line and PARPi-sensitive ATM mutant NSCLC PDX models, suggesting a downstream effect on the impairment of HR-mediated DNA checkpoint signaling. Further analysis revealed that loss of ATM led to inhibition of phosphorylation of MRN (Mre11-Rad50-NBS1) complex proteins, which are required for ATM-mediated downstream phosphorylation of p53, BRCA1, and CHK2. Conclusions: Taken together, our findings highlight that the synthetic lethality of niraparib in ATM-deficient tumors can be regulated through a subsequent effect on the modulation of BRCA1/2 expression and its effect on HR function.

Grazing livestock move by Lévy walks: Implications for soil health and environment.

Grazing livestock plays an important role in the context of food security, agricultural sustainability and climate change. Understanding how livestock move and interact with their environment may offer new insights on how grazing practices impact soil and ecosystem functions at spatial and temporal scales where knowledge is currently limited. We characterized daily and seasonal grazing patterns using Global Positioning System (GPS) data from two grazing strategies: conventionally- and rotationally-grazed pastures. Livestock movement was consistent with the so-called Lévy walks, and could thus be simulated with Lévy-walk based probability density functions. Our newly introduced "Moovement model" links grazing patterns with soil structure and related functions by coupling animal movement and soil structure dynamics models, allowing to predict spatially-explicit changes in key soil properties. Predicted post-grazing management-specific bulk densities were consistent with field measurements and confirmed that rotational grazing produced similar disturbance as conventional grazing despite hosting higher stock densities. Harnessing information on livestock movement and its impacts in soil structure within a modelling framework can help testing and optimizing grazing strategies for ameliorating their impact on soil health and environment.

Bioenergy crop production and carbon sequestration potential under changing climate and land use: A case study in the upper River Taw catchment in southwest England.

Reductions in CO2 emissions are essential to support the UK in achieving its net zero policy objective by around mid-century. Both changing climate and land use change (LUC) offer an opportunity to deploy suitable bioenergy crops strategically to enhance energy production and C sequestration to help deliver net zero through capturing atmospheric CO2. Against this background, we applied process-based models to evaluate the extent of net primary productivity (NPP) losses/gains associated with perennial bioenergy crops and to assess their C sequestration potential under changing climate in the upper River Taw observatory catchment in southwest England. In so doing, we also determined whether LUC from permanent grassland to perennial bioenergy crops, considered in this study, can increase the production and C sequestration potential in the study area. The results show that a warming climate positively impacts the production of all crops considered (permanent grassland, Miscanthus and two cultivars of short rotation coppice (SRC) willow). Overall, Miscanthus provides higher aboveground biomass for energy compared to willow and grassland whereas the broadleaf willow cultivar 'Endurance' is best suited, among all crops considered, for C sequestration in this environment, and more so in the changing climate. In warmer lowlands, LUC from permanent grassland to Miscanthus and in cooler uplands from permanent grassland to 'Endurance', enhances NPP. Colder areas are predicted to benefit more from changing climate in terms of above and belowground biomass for both Miscanthus and willow. The study shows that the above LUC can help augment non-fossil energy production and increase C sequestration potential if C losses from land conversion do not exceed the benefits from LUC. In the wake of a changing climate, aboveground biomass for bioenergy and belowground biomass to enhance carbon sequestration can be managed by the careful selection of bioenergy crops and targeted deployment within certain climatic zones.

Deoxycytidine kinase (dCK) inhibition is synthetic lethal with BRCA2 deficiency.

BRCA2 is a well-established cancer driver in several human malignancies. While the remarkable success of PARP inhibitors proved the clinical potential of targeting BRCA deficiencies, the emergence of resistance mechanisms underscores the importance of seeking novel Synthetic Lethal (SL) targets for future drug development efforts. In this work, we performed a BRCA2-centric SL screen with a collection of plant-derived compounds from South America. We identified the steroidal alkaloid Solanocapsine as a selective SL inducer, and we were able to substantially increase its potency by deriving multiple analogs. The use of two complementary chemoproteomic approaches led to the identification of the nucleotide salvage pathway enzyme deoxycytidine kinase (dCK) as Solanocapsine's target responsible for its BRCA2-linked SL induction. Additional confirmatory evidence was obtained by using the highly specific dCK inhibitor (DI-87), which induces SL in multiple BRCA2-deficient and KO contexts. Interestingly, dCK-induced SL is mechanistically different from the one induced by PARP inhibitors. dCK inhibition generates substantially lower levels of DNA damage, and cytotoxic phenotypes are associated exclusively with mitosis, thus suggesting that the fine-tuning of nucleotide supply in mitosis is critical for the survival of BRCA2-deficient cells. Moreover, by using a xenograft model of contralateral tumors, we show that dCK impairment suffices to trigger SL in-vivo. Taken together, our findings unveil dCK as a promising new target for BRCA2-deficient cancers, thus setting the ground for future therapeutic alternatives to PARP inhibitors.

An overlooked mechanism underlying the attenuated temperature response of soil heterotrophic respiration.

Biogeochemical reactions occurring in soil pore space underpin gaseous emissions measured at macroscopic scales but are difficult to quantify due to their complexity and heterogeneity. We develop a volumetric-average method to calculate aerobic respiration rates analytically from soil with microscopic soil structure represented explicitly. Soil water content in the model is the result of the volumetric-average of the microscopic processes, and it is nonlinearly coupled with temperature and other factors. Since many biogeochemical reactions are driven by oxygen (O2) which must overcome various resistances before reaching reactive microsites from the atmosphere, the volumetric-average results in negative feedback between temperature and soil respiration, with the magnitude of the feedback increasing with soil water content and substrate quality. Comparisons with various experiments show the model reproduces the variation of carbon dioxide emission from soils under different water content and temperature gradients, indicating that it captures the key microscopic processes underpinning soil respiration. We show that alongside thermal microbial adaptation, substrate heterogeneity and microbial turnover and carbon use efficiency, O2 dissolution and diffusion in water associated with soil pore space is another key explanation for the attenuated temperature response of soil respiration and should be considered in developing soil organic carbon models.

Exploring the effects of land management change on productivity, carbon and nutrient balance: Application of an Ensemble Modelling Approach to the upper River Taw observatory, UK.

Agriculture is challenged to produce healthy food and to contribute to cleaner energy whilst mitigating climate change and protecting ecosystems. To achieve this, policy-driven scenarios need to be evaluated with available data and models to explore trade-offs with robust accounting for the uncertainty in predictions. We developed a novel model ensemble using four complementary state-of-the-art agroecosystems models to explore the impacts of land management change. The ensemble was used to simulate key agricultural and environmental outputs under various scenarios for the upper River Taw observatory, UK. Scenarios assumed (i) reducing livestock production whilst simultaneously increasing the area of arable where it is feasible to cultivate (PG2A), (ii) reducing livestock production whilst simultaneously increasing bioenergy production in areas of the catchment that are amenable to growing bioenergy crops (PG2BE) and (iii) increasing both arable and bioenergy production (PG2A + BE). Our ensemble approach combined model uncertainty using the tower property of expectation and the law of total variance. Results show considerable uncertainty for predicted nutrient losses with different models partitioning the uncertainty into different pathways. Bioenergy crops were predicted to produce greatest yields from Miscanthus in lowland and from SRC-willow (cv. Endurance) in uplands. Each choice of management is associated with trade-offs; e.g. PG2A results in a significant increase of edible calories (6736 Mcal ha-1) but reduced soil C (-4.32 t C ha-1). Model ensembles in the agroecosystem context are difficult to implement due to challenges of model availability and input and output alignment. Despite these challenges, we show that ensemble modelling is a powerful approach for applications such as ours, offering benefits such as capturing structural as well as data uncertainty and allowing greater combinations of variables to be explored. Furthermore, the ensemble provides a robust means for combining uncertainty at different scales and enables us to identify weaknesses in system understanding.

The potential for soybean to diversify the production of plant-based protein in the UK.

Soybean (Glycine max) offers an important source of plant-based protein. Currently much of Europe's soybean is imported, but there are strong economic and agronomic arguments for boosting local production. Soybean is grown in central and eastern Europe but is less favoured in the North due to climate. We conducted field trials across three seasons and two sites in the UK to test the viability of early-maturing soybean varieties and used the data from these trials to calibrate and validate the Rothamsted Landscape Model. Once validated, the model was used to predict the probability soybean would mature and the associated yield for 26 sites across the UK based on weather data under current, near-future (2041-60) and far-future (2081-2100) climate. Two representative concentration pathways, a midrange mitigation scenario (RCP4.5) and a high emission scenario (RCP8.5) were also explored. Our analysis revealed that under current climate early maturing varieties will mature in the south of the UK, but the probability of failure increases with latitude. Of the 26 sites considered, only at one did soybean mature for every realisation. Predicted expected yields ranged between 1.39 t ha-1 and 1.95 t ha-1 across sites. Under climate change these varieties are likely to mature as far north as southern Scotland. With greater levels of CO2, yield is predicted to increase by as much as 0.5 t ha-1 at some sites in the far future, but this is tempered by other effects of climate change meaning that for most sites no meaningful increase in yield is expected. We conclude that soybean is likely to be a viable crop in the UK and for similar climates at similar latitudes in Northern Europe in the future but that for yields to be economically attractive for local markets, varieties must be chosen to align with the growing season.

Model-based optimisation of agricultural profitability and nutrient management: a practical approach for dealing with issues of scale.

To manage agricultural landscapes more sustainably, we must understand and quantify the synergies and trade-offs between environmental impact, production, and other ecosystem services. Models play an important role in this type of analysis as generally it is infeasible to test multiple scenarios by experiment. These models can be linked with algorithms that optimise for multiple objectives by searching a space of allowable management interventions (the control variables). Optimisation of landscapes for multiple objectives can be computationally challenging, however, particularly if the scale of management is typically smaller (e.g. field scale) than the scale at which the objective is quantified (landscape scale) resulting in a large number of control variables whose impacts do not necessarily scale linearly. In this paper, we explore some practical solutions to this problem through a case study. In our case study, we link a relatively detailed, agricultural landscape model with a multiple-objective optimisation algorithm to determine solutions that both maximise profitability and minimise greenhouse gas emissions in response to management. The optimisation algorithm combines a non-dominated sorting routine with differential evolution, whereby a 'population' of 100 solutions evolves over time to a Pareto optimal front. We show the advantages of using a hierarchical approach to the optimisation, whereby it is applied to finer-scale units first (i.e. fields), and then the solutions from each optimisation are combined in a second step to produce landscape-scale outcomes. We show that if there is no interaction between units, then the solution derived using such an approach will be the same as the one obtained if the landscape is optimised in one step. However, if there is spatial interaction, or if there are constraints on the allowable sets of solutions, then outcomes can be quite different. In these cases, other approaches to increase the efficiency of the optimisation may be more appropriate-such as initialising the control variables for half of the population of solutions with values expected to be near optimal. Our analysis shows the importance of aligning a policy or management recommendation with the appropriate scale.

Soil as an extended composite phenotype of the microbial metagenome.

We use a unique set of terrestrial experiments to demonstrate how soil management practises result in emergence of distinct associations between physical structure and biological functions. These associations have a significant effect on the flux, resilience and efficiency of nutrient delivery to plants (including water). Physical structure, determining the air-water balance in soil as well as transport rates, is influenced by nutrient and physical interventions. Contrasting emergent soil structures exert selective pressures upon the microbiome metagenome. These selective pressures are associated with the quality of organic carbon inputs, the prevalence of anaerobic microsites and delivery of nutrients to microorganisms attached to soil surfaces. This variety results in distinctive gene assemblages characterising each state. The nature of the interactions provide evidence that soil behaves as an extended composite phenotype of the resident microbiome, responsive to the input and turnover of plant-derived organic carbon. We provide new evidence supporting the theory that soil-microbe systems are self-organising states with organic carbon acting as a critical determining parameter. This perspective leads us to propose carbon flux, rather than soil organic carbon content as the critical factor in soil systems, and we present evidence to support this view.

Associations Between Social Anxiety and Affective and Empathic Forecasts: A Replication and Extension in a Mechanical Turk Sample.

People often overestimate the intensity and duration of their future emotions, referred to as an impact bias. Impact biases have been documented in predictions people make about their own emotions, as well as the others' emotions (i.e., affective and empathic forecasting, respectively). Recent studies have shown that negative impact biases may be stronger, and positive impact biases may be attenuated, in individuals with symptoms of social anxiety. The current study sought to replicate and extend these findings in a Mechanical Turk (MTurk) sample. MTurk is a particularly interesting online platform for such research because of the unusually high prevalence of social anxiety among MTurk users. Within a computer-based survey, 93 MTurk users read vignettes in which a second-person narrator elicited either disgust, anger, or happiness from another person. After each vignette, participants predicted how the narrator (i.e., affective forecasts) and the other person (i.e., empathic forecasts) would feel. Overall, results confirmed the existence of associations between social anxiety symptoms and negative affective and empathic forecasting biases, though no significant relations were found between social anxiety symptoms and positive forecasting biases. Negative affective and empathic forecasting biases were significantly correlated. Age and gender were also examined as potential predictors and moderators of hypothesized effects. Though younger age and female gender were associated with specific forecast ratings, controlling for these variables did not alter the associations between social anxiety and affective or empathic forecasts and no moderation effects were found. Overall, results provide additional support for the relevance of impact biases to social anxiety and suggest that they may be useful targets of intervention.

Optimizing multifunctional agroecosystems in irrigated dryland agriculture to restore soil carbon - Experiments and modelling.

Irrigated dryland agroecosystems could become more sustainable if crop and soil management enhanced soil organic carbon (SOC). We hypothesized that combining high inputs from cover crops with no-tillage will increase long-term SOC stocks. Caatinga shrublands had been cleared in 1972 for arable crops and palm plantations before implementing field experiments on Mango and Melon systems (established in 2009 and 2012, respectively). Each of the two experiments were managed with no-till (NT) or conventional till (CT), and three types of cover cropping, either a plant mixture of 75% (PM1) or 25% (PM2) legumes, or spontaneous vegetation (SV). The RothC model was used with a daily timestep to simulate the soil moisture dynamics and C turnover for this dry climate. Carbon inputs were between 2.62 and 5.82 Mg C ha-1 year-1 and increased the depleted SOC stocks by 0.08 to 0.56 Mg C ha-1 year-1. Scenarios of continuous biomass inputs of ca. 5 Mg C ha-1 year-1 for 60 years are likely to increase SOC stocks in the mango NT beyond the original Caatinga SOC by between 19.2 and 20.5 Mg C ha-1. Under CT similar inputs would increase SOC stocks only marginally above depletion (2.75 to 2.47 Mg C ha-1). Under melon, annual carbon inputs are slightly greater (up to 5.5 Mg C ha-1 year-1) and SOC stocks would increase on average by another 8% to 22.3 to 20.6 Mg C ha-1 under NT and by 8 Mg C ha-1 under CT. These long-term simulations show that combining NT with high quality cover crops (PM1, PM2) would exceed SOC stocks of the initial Caatinga within 20 and 25 years under irrigated melon and mango cultivation, respectively. These results present a solution to reverse prior loss of SOC by replacing CT dryland agriculture with irrigated NT plus high input cover crops agroecosystems.

Multi-objective optimization as a tool to identify possibilities for future agricultural landscapes.

Agricultural landscapes provide many functions simultaneously including food production, regulation of water and regulation of greenhouse gases. Thus, it is challenging to make land management decisions, particularly transformative changes, that improve on one function without unintended consequences for other functions. To make informed decisions the trade-offs between different landscape functions must be considered. Here, we use a multi-objective optimization algorithm with a model of crop production that also simulates environmental effects such as nitrous oxide emissions to identify trade-off frontiers and associated possibilities for agricultural management. Trade-offs are identified in three soil types, using wheat production in the UK as an example, then the trade-off for combined management of the three soils is considered. The optimization algorithm identifies trade-offs between different objectives and allows them to be visualised. For example, we observed a highly non-linear trade-off between wheat yield and nitrous oxide emissions, illustrating where small changes might have a large impact. We used a cluster analysis to identify distinct management strategies with similar management actions and use these clusters to link the trade-off curves to possibilities for management. There were more possible strategies for achieving desirable environmental outcomes and remaining profitable when the management of different soil types was considered together. Interestingly, it was on the soil capable of the highest potential profit that lower profit strategies were identified as useful for combined management. Meanwhile, to maintain average profitability across the soils, it was necessary to maximise the profit from the soil with the lowest potential profit. These results are somewhat counterintuitive and so the range of strategies supplied by the model could be used to stimulate discussion amongst stakeholders. In particular, as some key objectives can be met in different ways, stakeholders could discuss the impact of these management strategies on other objectives not quantified by the model.

Is ADHD, independent of ODD, associated with whether and why college students misuse stimulant medication?

Although previous research suggests that undergraduates with untreated or undertreated attention-deficit/hyperactivity disorder (ADHD) symptoms may have academic motives for stimulant medication misuse, no previous work has examined the relation of ADHD symptoms, controlling for comorbid oppositional defiant disorder (ODD), to misuse, or has explored how these symptoms are differentially related to motives for misuse. Among a sample of 900 students from one public university, the current study first tested whether increased ADHD symptomology (using the Current Symptoms Scale, CSS) was associated with an increased likelihood of misusing stimulant medication, controlling for comorbid ODD. We then examined whether those with increased ADHD symptomology were more likely to report academic motives for misuse. The prevalence rate of misuse in the past year was 22%. Participants who met symptom count criteria for ADHD (controlling for comorbid ODD) were 2.90 times more likely to misuse stimulant medication than those who did not. Among misusers, those who met ADHD criteria were also 2.80 times more likely to report academic motives for misuse. These results support that stimulant medication misuse is likely driven, in part, by inadequate or absent care for the executive functioning impairments associated with ADHD. Therefore, a greater focus on assessment and treatment of college students with ADHD symptoms is warranted. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Impact of two centuries of intensive agriculture on soil carbon, nitrogen and phosphorus cycling in the UK.

This paper describes an agricultural model (Roth-CNP) that estimates carbon (C), nitrogen (N) and phosphorus (P) pools, pool changes, their balance and the nutrient fluxes exported from arable and grassland systems in the UK during 1800-2010. The Roth-CNP model was developed as part of an Integrated Model (IM) to simulate C, N and P cycling for the whole of UK, by loosely coupling terrestrial, hydrological and hydro-chemical models. The model was calibrated and tested using long term experiment (LTE) data from Broadbalk (1843) and Park Grass (1856) at Rothamsted. We estimated C, N and P balance and their fluxes exported from arable and grassland systems on a 5km×5km grid across the whole of UK by using the area of arable of crops and livestock numbers in each grid and their management. The model estimated crop and grass yields, soil organic carbon (SOC) stocks and nutrient fluxes in the form of NH4-N, NO3-N and PO4-P. The simulated crop yields were compared to that reported by national agricultural statistics for the historical to the current period. Overall, arable land in the UK have lost SOC by -0.18, -0.25 and -0.08MgCha-1y-1 whereas land under improved grassland SOC stock has increased by 0.20, 0.47 and 0.24MgCha-1y-1 during 1800-1950, 1950-1970 and 1970-2010 simulated in this study. Simulated N loss (by leaching, runoff, soil erosion and denitrification) increased both under arable (-15, -18 and -53kgNha-1y-1) and grass (-18, -22 and -36kgNha-1y-1) during different time periods. Simulated P surplus increased from 2.6, 10.8 and 18.1kgPha-1y-1 under arable and 2.8, 11.3 and 3.6kgPha-1y-1 under grass lands 1800-1950, 1950-1970 and 1970-2010.

Protein targeting chimeric molecules specific for bromodomain and extra-terminal motif family proteins are active against pre-clinical models of multiple myeloma.

Bromodomain and extraterminal (BET) domain containing protein (BRD)-4 modulates the expression of oncogenes such as c-myc, and is a promising therapeutic target in diverse cancer types. We performed pre-clinical studies in myeloma models with bi-functional protein-targeting chimeric molecules (PROTACs) which target BRD4 and other BET family members for ubiquitination and proteasomal degradation. PROTACs potently reduced the viability of myeloma cell lines in a time-dependent and concentration-dependent manner associated with G0/G1 arrest, reduced levels of CDKs 4 and 6, increased p21 levels, and induction of apoptosis. These agents specifically decreased cellular levels of downstream BRD4 targets, including c-MYC and N-MYC, and a Cereblon-targeting PROTAC showed downstream effects similar to those of an immunomodulatory agent. Notably, PROTACs overcame bortezomib, dexamethasone, lenalidomide, and pomalidomide resistance, and their activity was maintained in otherwise isogenic myeloma cells with wild-type or deleted TP53. Combination studies showed synergistic interactions with dexamethasone, BH3 mimetics, and Akt pathway inhibitors. BET-specific PROTACs induced a rapid loss of viability of primary cells from myeloma patients, and delayed growth of MM1.S-based xenografts. Our data demonstrate that BET degraders have promising activity against pre-clinical models of multiple myeloma, and support their translation to the clinic for patients with relapsed and/or refractory disease.

Identification and Characterization of Von Hippel-Lindau-Recruiting Proteolysis Targeting Chimeras (PROTACs) of TANK-Binding Kinase 1.

Proteolysis targeting chimeras (PROTACs) are bifunctional molecules that recruit an E3 ligase to a target protein to facilitate ubiquitination and subsequent degradation of that protein. While the field of targeted degraders is still relatively young, the potential for this modality to become a differentiated and therapeutic reality is strong, such that both academic and pharmaceutical institutions are now entering this interesting area of research. In this article, we describe a broadly applicable process for identifying degrader hits based on the serine/threonine kinase TANK-binding kinase 1 (TBK1) and have generalized the key structural elements associated with degradation activities. Compound 3i is a potent hit (TBK1 DC50 = 12 nM, Dmax = 96%) with excellent selectivity against a related kinase IKKε, which was further used as a chemical tool to assess TBK1 as a target in mutant K-Ras cancer cells.

The landscape model: A model for exploring trade-offs between agricultural production and the environment.

We describe a model framework that simulates spatial and temporal interactions in agricultural landscapes and that can be used to explore trade-offs between production and environment so helping to determine solutions to the problems of sustainable food production. Here we focus on models of agricultural production, water movement and nutrient flow in a landscape. We validate these models against data from two long-term experiments, (the first a continuous wheat experiment and the other a permanent grass-land experiment) and an experiment where water and nutrient flow are measured from isolated catchments. The model simulated wheat yield (RMSE 20.3-28.6%), grain N (RMSE 21.3-42.5%) and P (RMSE 20.2-29% excluding the nil N plots), and total soil organic carbon particularly well (RMSE3.1-13.8%), the simulations of water flow were also reasonable (RMSE 180.36 and 226.02%). We illustrate the use of our model framework to explore trade-offs between production and nutrient losses.

PROTAC-induced BET protein degradation as a therapy for castration-resistant prostate cancer.

Prostate cancer has the second highest incidence among cancers in men worldwide and is the second leading cause of cancer deaths of men in the United States. Although androgen deprivation can initially lead to remission, the disease often progresses to castration-resistant prostate cancer (CRPC), which is still reliant on androgen receptor (AR) signaling and is associated with a poor prognosis. Some success against CRPC has been achieved by drugs that target AR signaling, but secondary resistance invariably emerges, and new therapies are urgently needed. Recently, inhibitors of bromodomain and extra-terminal (BET) family proteins have shown growth-inhibitory activity in preclinical models of CRPC. Here, we demonstrate that ARV-771, a small-molecule pan-BET degrader based on proteolysis-targeting chimera (PROTAC) technology, demonstrates dramatically improved efficacy in cellular models of CRPC as compared with BET inhibition. Unlike BET inhibitors, ARV-771 results in suppression of both AR signaling and AR levels and leads to tumor regression in a CRPC mouse xenograft model. This study is, to our knowledge, the first to demonstrate efficacy with a small-molecule BET degrader in a solid-tumor malignancy and potentially represents an important therapeutic advance in the treatment of CRPC.