FINO2 initiates ferroptosis through GPX4 inactivation and iron oxidation.

Ferroptosis is a non-apoptotic form of regulated cell death caused by the failure of the glutathione-dependent lipid-peroxide-scavenging network. FINO2 is an endoperoxide-containing 1,2-dioxolane that can initiate ferroptosis selectively in engineered cancer cells. We investigated the mechanism and structural features necessary for ferroptosis initiation by FINO2. We found that FINO2 requires both an endoperoxide moiety and a nearby hydroxyl head group to initiate ferroptosis. In contrast to previously described ferroptosis inducers, FINO2 does not inhibit system xc- or directly target the reducing enzyme GPX4, as do erastin and RSL3, respectively, nor does it deplete GPX4 protein, as does FIN56. Instead, FINO2 both indirectly inhibits GPX4 enzymatic function and directly oxidizes iron, ultimately causing widespread lipid peroxidation. These findings suggest that endoperoxides such as FINO2 can initiate a multipronged mechanism of ferroptosis.

Satellite-based aerosol optical depth estimates over the continental U.S. during the 2020 wildfire season: Roles of smoke and land cover.

Wildfires produce smoke that can affect an area >1000 times the burn extent, with far-reaching human health, ecologic, and economic impacts. Accurately estimating aerosol load within smoke plumes is therefore crucial for understanding and mitigating these impacts. We evaluated the effectiveness of the latest Collection 6.1 MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm in estimating aerosol optical depth (AOD) across the U.S. during the historic 2020 wildfire season. We compared satellite-based MAIAC AOD to ground-based AERONET AOD measurements during no-, light-, medium-, and heavy-smoke conditions identified using the Hazard Mapping System Fire and Smoke Product. This smoke product consists of maximum extent smoke polygons digitized by analysts using visible band imagery and classified according to smoke density. We also examined the strength of the correlations between satellite- and ground-based AOD for major land cover types under various smoke density levels. MAIAC performed well in estimating AOD during smoke-affected conditions. Correlations between MAIAC and AERONET AOD were strong for medium- (r = 0.91) and heavy-smoke (r = 0.90) density, and MAIAC estimates of AOD showed little bias relative to ground-based AERONET measurements (normalized mean bias = 3 % for medium, 5 % for heavy smoke). During two high AOD, heavy smoke episodes, MAIAC underestimated ground-based AERONET AOD under mixed aerosol (i.e., smoke and dust; median bias = -0.08) and overestimated AOD under smoke-dominated (median bias = 0.02) aerosol. MAIAC most overestimated ground-based AERONET AOD over barren land (mean NMB = 48 %). Our findings indicate that MODIS MAIAC can provide robust estimates of AOD as smoke density increases in coming years. Increased frequency of mixed aerosol and expansion of developed land could affect the performance of the MAIAC algorithm in the future, however, with implications for evaluating wildfire-associated health and welfare effects and air quality standards.

Subtype-selective prenylated isoflavonoids disrupt regulatory drivers of MYCN-amplified cancers.

Transcription factors have proven difficult to target with small molecules because they lack pockets necessary for potent binding. Disruption of protein expression can suppress targets and enable therapeutic intervention. To this end, we developed a drug discovery workflow that incorporates cell-line-selective screening and high-throughput expression profiling followed by regulatory network analysis to identify compounds that suppress regulatory drivers of disease. Applying this approach to neuroblastoma (NBL), we screened bioactive molecules in cell lines representing its MYC-dependent (MYCNA) and mesenchymal (MES) subtypes to identify selective compounds, followed by PLATESeq profiling of treated cells. This revealed compounds that disrupt a sub-network of MYCNA-specific regulatory proteins, resulting in MYCN degradation in vivo. The top hit was isopomiferin, a prenylated isoflavonoid that inhibited casein kinase 2 (CK2) in cells. Isopomiferin and its structural analogs inhibited MYC and MYCN in NBL and lung cancer cells, highlighting the general MYC-inhibiting potential of this unique scaffold.

Reconstructing aerosol optical depth using spatiotemporal Long Short-Term Memory convolutional autoencoder.

Aerosol Optical Depth (AOD) is a crucial atmospheric parameter in comprehending climate change, air quality, and its impacts on human health. Satellites offer exceptional spatiotemporal AOD data continuity. However, data quality is influenced by various atmospheric, landscape, and instrumental factors, resulting in data gaps. This study presents a new solution to this challenge by providing a long-term, gapless satellite-derived AOD dataset for Texas from 2010 to 2022, utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-angle Implementation of Atmospheric Correction (MAIAC) products. Missing AOD data were reconstructed using a spatiotemporal Long Short-Term Memory (LSTM) convolutional autoencoder. Evaluation against an independent test dataset demonstrated the model's effectiveness, with an average Root Mean Square Error (RMSE) of 0.017 and an R2 value of 0.941. Validation against the ground-based AERONET dataset indicated satisfactory agreement, with RMSE values ranging from 0.052 to 0.067. The reconstructed AOD data are available at daily, monthly, quarterly, and yearly scales, providing a valuable resource to advance understanding of the Earth's atmosphere and support decision-making concerning air quality and public health.

Integrating low-cost sensor monitoring, satellite mapping, and geospatial artificial intelligence for intra-urban air pollution predictions.

There is a growing need to apply geospatial artificial intelligence analysis to disparate environmental datasets to find solutions that benefit frontline communities. One such critically needed solution is the prediction of health-relevant ambient ground-level air pollution concentrations. However, many challenges exist surrounding the size and representativeness of limited ground reference stations for model development, reconciling multi-source data, and interpretability of deep learning models. This research addresses these challenges by leveraging a strategically deployed, extensive low-cost sensor (LCS) network that was rigorously calibrated through an optimized neural network. A set of raster predictors with varying data quality and spatial scales was retrieved and processed, including gap-filled satellite aerosol optical depth products and airborne LiDAR-derived 3D urban form. We developed a multi-scale, attention-enhanced convolutional neural network model to reconcile the LCS measurements and multi-source predictors for estimating daily PM2.5 concentration at 30-m resolution. This model employs an advanced approach by using the geostatistical kriging method to generate a baseline pollution pattern and a multi-scale residual method to identify both regional patterns and localized events for high-frequency feature retention. We further used permutation tests to quantify the feature importance, which has rarely been done in DL applications in environmental science. Finally, we demonstrated one application of the model by investigating the air pollution inequality issue across and within various urbanization levels at the block group scale. Overall, this research demonstrates the potential of geospatial AI analysis to provide actionable solutions for addressing critical environmental issues.

Development of optimized drug-like small molecule inhibitors of the SARS-CoV-2 3CL protease for treatment of COVID-19.

The SARS-CoV-2 3CL protease is a critical drug target for small molecule COVID-19 therapy, given its likely druggability and essentiality in the viral maturation and replication cycle. Based on the conservation of 3CL protease substrate binding pockets across coronaviruses and using screening, we identified four structurally distinct lead compounds that inhibit SARS-CoV-2 3CL protease. After evaluation of their binding specificity, cellular antiviral potency, metabolic stability, and water solubility, we prioritized the GC376 scaffold as being optimal for optimization. We identified multiple drug-like compounds with <10 nM potency for inhibiting SARS-CoV-2 3CL and the ability to block SARS-CoV-2 replication in human cells, obtained co-crystal structures of the 3CL protease in complex with these compounds, and determined that they have pan-coronavirus activity. We selected one compound, termed coronastat, as an optimized lead and characterized it in pharmacokinetic and safety studies in vivo. Coronastat represents a new candidate for a small molecule protease inhibitor for the treatment of SARS-CoV-2 infection for eliminating pandemics involving coronaviruses.

GTP Cyclohydrolase 1/Tetrahydrobiopterin Counteract Ferroptosis through Lipid Remodeling.

Ferroptosis is an iron-dependent form of regulated cell death linking iron, lipid, and glutathione levels to degenerative processes and tumor suppression. By performing a genome-wide activation screen, we identified a cohort of genes antagonizing ferroptotic cell death, including GTP cyclohydrolase-1 (GCH1) and its metabolic derivatives tetrahydrobiopterin/dihydrobiopterin (BH4/BH2). Synthesis of BH4/BH2 by GCH1-expressing cells caused lipid remodeling, suppressing ferroptosis by selectively preventing depletion of phospholipids with two polyunsaturated fatty acyl tails. GCH1 expression level in cancer cell lines stratified susceptibility to ferroptosis, in accordance with its expression in human tumor samples. The GCH1-BH4-phospholipid axis acts as a master regulator of ferroptosis resistance, controlling endogenous production of the antioxidant BH4, abundance of CoQ10, and peroxidation of unusual phospholipids with two polyunsaturated fatty acyl tails. This demonstrates a unique mechanism of ferroptosis protection that is independent of the GPX4/glutathione system.

Imidazole Ketone Erastin Induces Ferroptosis and Slows Tumor Growth in a Mouse Lymphoma Model.

Ferroptosis is a form of regulated cell death that can be induced by inhibition of the cystine-glutamate antiporter, system xc-. Among the existing system xc- inhibitors, imidazole ketone erastin (IKE) is a potent, metabolically stable inhibitor of system xc- and inducer of ferroptosis potentially suitable for in vivo applications. We investigated the pharmacokinetic and pharmacodynamic features of IKE in a diffuse large B cell lymphoma (DLBCL) xenograft model and demonstrated that IKE exerted an antitumor effect by inhibiting system xc-, leading to glutathione depletion, lipid peroxidation, and the induction of ferroptosis biomarkers both in vitro and in vivo. Using untargeted lipidomics and qPCR, we identified distinct features of lipid metabolism in IKE-induced ferroptosis. In addition, biodegradable polyethylene glycol-poly(lactic-co-glycolic acid) nanoparticles were employed to aid in IKE delivery and exhibited reduced toxicity compared with free IKE in a DLBCL xenograft model.

3D Visualization of the Temporal and Spatial Spread of Tau Pathology Reveals Extensive Sites of Tau Accumulation Associated with Neuronal Loss and Recognition Memory Deficit in Aged Tau Transgenic Mice.

3D volume imaging using iDISCO+ was applied to observe the spatial and temporal progression of tau pathology in deep structures of the brain of a mouse model that recapitulates the earliest stages of Alzheimer's disease (AD). Tau pathology was compared at four timepoints, up to 34 months as it spread through the hippocampal formation and out into the neocortex along an anatomically connected route. Tau pathology was associated with significant gliosis. No evidence for uptake and accumulation of tau by glia was observed. Neuronal cells did appear to have internalized tau, including in extrahippocampal areas as a small proportion of cells that had accumulated human tau protein did not express detectible levels of human tau mRNA. At the oldest timepoint, mature tau pathology in the entorhinal cortex (EC) was associated with significant cell loss. As in human AD, mature tau pathology in the EC and the presence of tau pathology in the neocortex correlated with cognitive impairment. 3D volume imaging is an ideal technique to easily monitor the spread of pathology over time in models of disease progression.