Cheng, J. D. | bioRxiv Science

bioRxiv2021

Neurons burdened by DNA double strand breaks incite microglia activation through antiviral-like signaling in neurodegeneration.

DNA double strand breaks (DSBs) are linked to aging, neurodegeneration, and senescence1,2. However, the role played by neurons burdened with DSBs in disease-associated neuroinflammation is not well understood. Here, we isolate neurons harboring DSBs from the CK-p25 mouse model of neurodegeneration through fluorescence-activated nuclei sorting (FANS), and characterize their transcriptomes using single-nucleus, bulk, and spatial sequencing techniques. We find that neurons harboring DSBs enter a late-stage DNA damage response marked by the activation of senescent and antiviral-like immune pathways. We identify the NFkB transcription factor as a master regulator of immune gene expression in DSB-bearing neurons, and find that the expression of cytokines like Cxcl10 and Ccl2 develop in DSB-bearing neurons before glial cell types. Alzheimers Disease pathology is significantly associated with immune activation in excitatory neurons, and direct purification of DSB-bearing neurons from Alzheimers Disease brain tissue further validates immune gene upregulation. Spatial transcriptomics reveal that regions of brain tissue dense with DSB-bearing neurons also harbor signatures of inflammatory microglia, which is ameliorated by NFkB knock down in neurons. Inhibition of NFkB or depletion of Ccl2 and Cxcl10 in DSB-bearing neurons also reduces microglial activation in organotypic brain slice culture. In conclusion, we find that in the context of age-associated neurodegenerative disease, DSBs activate immune pathways in neurons, which in turn adopt a senescence associated secretory phenotype to elicit microglia activation. These findings highlight a novel role for neurons in the mechanism of age-associated neuroinflammation. SummaryIt is unclear how age-associated DNA double strand break (DSB) accumulation in neurons influences the progression of cellular senescence and neurodegenerative disease. Here, we leverage mouse models of neurodegeneration, single-nucleus, bulk, and spatial transcriptomics from Alzheimers disease patients, mouse models, and primary neuron cultures to dissect the immune signaling pathways initiated by DSB-bearing neurons that trigger neuroinflammation.

neuroscience↗

bioRxiv2017

The Trauma Severity Model: An Ensemble Learning Approach to Risk Prediction

Statistical theory indicates that a flexible model can attain a lower generalization error than an inflexible model, provided that the setting is appropriate. This is highly relevant in the context of mortality risk prediction for trauma patients, as researchers have focused exclusively on the use of generalized linear models for risk prediction, and generalized linear models may be too inflexible to capture the potentially complex relationships in trauma data. Due to this, we propose a machine learning model, the Trauma Severity Model (TSM), for risk prediction. In order to validate TSMs performance, this study compares TSM to three established risk prediction models: the Bayesian Logistic Injury Severity Score, the Harborview Assessment for Risk of Mortality, and the Trauma Mortality Prediction Model. Our results indicate that TSM has superior performance, and thereby provides improved risk prediction.\n\nHighlightsO_LIWe propose an ensemble machine learning model for trauma risk prediction.\nC_LIO_LIA hyper-parameter search scheme is proposed for model development.\nC_LIO_LIWe compare our model to established models for trauma risk prediction.\nC_LIO_LIOur model improves over established models for each performance metric considered.\nC_LI

epidemiology↗