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Biology subjects

Trotta, C.

Publications and source records attributed to Trotta, C..

4 recordsLinked to original sources

No leeway to enhance carbon sequestration and stock capacity via changes to forest management

Forest management practices might act as nature-based methods to remove CO2 from the atmosphere and slow anthropogenic climate change and thus support an EU forest-based climate change mitigation strategy. However, the extent to which diversified management actions could lead to quantitatively important changes in carbon sequestration and stocking capacity at the tree level remains to be thoroughly assessed. To that end, we used a state-of-the-science bio-geochemically based forest growth model to simulate effects of multiple forest management scenarios on net primary productivity (NPP) and potential carbon woody stocks (pCWS) under twenty scenarios of climate change in a suite of observed and virtual forest stands in temperate and boreal European forests. Previous modelling experiments indicated that the capacity of forests to assimilate and store atmospheric CO2 in woody biomass is already being attained under business-as-usual forest management practices across a range of climate change scenarios. Nevertheless, we find that on the long-term, with increasing atmospheric CO2 concentration and warming, managed forests show both higher productivity capacity and a larger potential pool size of stored carbon than unmanaged forests as long as thinning and tree harvesting are of moderate intensity.

ecology↗

Ecosystem photosynthesis in land-surface models: a first-principles approach

Vegetation regulates land-atmosphere water and energy exchanges and is an essential component of land-surface models (LSMs). However, LSMs have been handicapped by assumptions that equate acclimated photosynthetic responses to environment with fast responses observable in the laboratory. These time scales can be distinguished by including specific representations of acclimation, but at the cost of further increasing parameter requirements. Here we develop an alternative approach based on optimality principles that predict the acclimation of carboxylation and electron-transport capacities, and a variable controlling the response of leaf-level carbon dioxide drawdown to vapour pressure deficit (VPD), to variations in growth conditions on a weekly to monthly time scale. In the "P model", an optimality-based light-use efficiency model for gross primary production (GPP) on this time scale, these acclimated responses are implicit. Here they are made explicit, allowing fast and slow response time-scales to be separated and GPP to be simulated at sub-daily timesteps. The resulting model mimics diurnal cycles of GPP recorded by eddy-covariance flux towers in a temperate grassland and boreal, temperate and tropical forests, with no parameter changes between biomes. Best performance is achieved when biochemical capacities are adjusted to match recent midday conditions. This model suggests a simple and parameter-sparse method to include both instantaneous and acclimated responses within an LSM framework, with many potential applications in weather, climate and carbon - cycle modelling. Plain Language SummaryVegetation regulates the exchanges of energy, water and carbon dioxide between the land and the atmosphere. Numerical climate models represent these processes, focusing mainly on their rapid variations in response to changes in the environment (including temperature and light) on timescales of seconds to hours. However, plants also adjust their physiology to environmental changes over longer periods within the season. Here we have adapted a simple model that formulates plant behaviour in terms of optimal trade-offs between different processes, so it simulates processes on both time scales. This model correctly reproduces the daily cycle of carbon dioxide uptake by plants, as recorded in different kinds of vegetation. We show that plants optimize their behaviour for midday conditions, when the light is greatest, and adjust to longer-term environmental variations on a timescale of about a week to a month. The model conveniently avoids the need to give specific, fixed values to physiological variables (such as photosynthetic capacity) for different types of plants. The optimality assumptions mean that the model gives equally good results in tropical, temperate and boreal forests, and in grasslands, using the same equations, and a very small number of input variables that are constant across the world. Key PointsO_LIOptimality theory is used to develop a simple model incorporating fast and acclimated responses of photosynthesis and stomatal conductance C_LIO_LIBiogeochemical photosynthetic capacities adjust to midday light conditions C_LIO_LIThe new model simulates gross primary production on sub-daily timesteps across a range of different vegetation types and climate C_LI

ecology↗

The DHODH Inhibitor PTC299 Arrests SARS-CoV-2 Replication and Suppresses Induction of Inflammatory Cytokines

The coronavirus disease 2019 (COVID-19) pandemic has created an urgent need for therapeutics that inhibit the SARS-CoV-2 virus and suppress the fulminant inflammation characteristic of advanced illness. Here, we describe the anti-COVID-19 potential of PTC299, an orally available compound that is a potent inhibitor of dihydroorotate dehydrogenase (DHODH), the rate-limiting enzyme of the de novo pyrimidine biosynthesis pathway. In tissue culture, PTC299 manifests robust, dose-dependent, and DHODH-dependent inhibition of SARS CoV-2 replication (EC50 range, 2.0 to 31.6 nM) with a selectivity index >3,800. PTC299 also blocked replication of other RNA viruses, including Ebola virus. Consistent with known DHODH requirements for immunomodulatory cytokine production, PTC299 inhibited the production of interleukin (IL)-6, IL-17A (also called IL-17), IL-17F, and vascular endothelial growth factor (VEGF) in tissue culture models. The combination of anti-SARS-CoV-2 activity, cytokine inhibitory activity, and previously established favorable pharmacokinetic and human safety profiles render PTC299 a promising therapeutic for COVID-19.

immunology↗

A deep learning approach to identify new gene targets of a novel therapeutic for human splicing disorders

Pre-mRNA splicing is a key control point in human gene expression. Disturbances in splicing due to mutation or aberrant splicing regulatory networks lead to dysregulated protein expression and contribute to a substantial fraction of human disease. Several classes of active and selective splicing modulator compounds have been recently identified, thus proving that pre-mRNA splicing is a viable target for therapy. We describe herein the identification of BPN-15477, a novel splicing modulator compound, that restores correct splicing of exon 20 in the Elongator complex protein 1 (ELP1) gene carrying the major IVS20+6T>C mutation responsible for familial dysautonomia. We then developed a machine learning approach to evaluate the therapeutic potential of BPN-15477 to correct splicing in other human genetic diseases. Using transcriptome sequencing from compound-treated fibroblast cells, we identified treatment responsive sequence signatures, the majority of which center at the 5 splice site of exons whose inclusion or exclusion is modulated by SMC treatment. We then leveraged this model to identify 155 human disease genes that harbor ClinVar mutations predicted to alter pre-mRNA splicing as potential targets for BPN-15477 treatment. Using in vitro splicing assays, we validated representative predictions by demonstrating successful correction of splicing defects caused by mutations in genes responsible for cystic fibrosis (CFTR), cholesterol ester storage disease (LIPA), Lynch syndrome (MLH1) and familial frontotemporal dementia (MAPT). Our study shows that deep learning techniques can identify a complex set of sequence signatures and predict response to pharmacological modulation, strongly supporting the use of in silico approaches to expand the therapeutic potential of drugs that modulate splicing.

bioinformatics↗