Excitotoxic inactivation of constitutive oxidative stress detoxification pathway in neurons can be rescued by PKD1

Excitotoxicity, a critical process in neurodegeneration, induces oxidative stress and neuronal death through mechanisms largely unknown. Since oxidative stress activates protein kinase D1 (PKD1) in tumor cells, we investigated the effect of excitotoxicity on neuronal PKD1 activity. Unexpectedly, we find that excitotoxicity provokes an early inactivation of PKD1 through a dephosphorylation-dependent mechanism mediated by protein phosphatase-1 (PP1) and dual specificity phosphatase-1 (DUSP1). This step turns off the IKK/NF-κB/SOD2 antioxidant pathway. Neuronal PKD1 inactivation by pharmacological inhibition or lentiviral silencing in vitro, or by genetic inactivation in neurons in vivo, strongly enhances excitotoxic neuronal death. In contrast, expression of an active dephosphorylation-resistant PKD1 mutant potentiates the IKK/NF-κB/SOD2 oxidative stress detoxification pathway and confers neuroprotection from in vitro and in vivo excitotoxicity. Our results indicate that PKD1 inactivation underlies excitotoxicity-induced neuronal death and suggest that PKD1 inactivation may be critical for the accumulation of oxidation-induced neuronal damage during aging and in neurodegenerative disorders

Transient Callosal Projections of L4 Neurons Are Eliminated for the Acquisition of Local Connectivity

Interhemispheric axons of the corpus callosum (CC) facilitate the higher order functions of the cerebral cortex. According to current views, callosal and non-callosal fates are determined early after a neuron's birth, and certain populations, such as cortical layer (L) 4 excitatory neurons of the primary somatosensory (S1) barrel, project only ipsilaterally. Using a novel axonal-retrotracing strategy and GFP-targeted visualization of Rorb+ neurons, we instead demonstrate that L4 neurons develop transient interhemispheric axons. Locally restricted L4 connectivity emerges when exuberant contralateral axons are refined in an area- and layer-specific manner during postnatal development. Surgical and genetic interventions of sensory circuits demonstrate that refinement rates depend on distinct inputs from sensory-specific thalamic nuclei. Reductions in input-dependent refinement result in mature functional interhemispheric hyperconnectivity, demonstrating the plasticity and bona fide callosal potential of L4 neurons. Thus, L4 neurons discard alternative interhemispheric circuits as instructed by thalamic input. This may ensure optimal wiring.This work was funded by grants from MINECO SAF2014-52119-R, BFU2016-81887-REDT, PCIN-2015-176-C02-02/ERA-Net Neuron (Era-Net,MINECO), MCIU/AEI/FEDER, UE SAF2017-83117-R.Peer reviewe

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024