Dataset Information

A RIPK1-regulated inflammatory microglial state in amyotrophic lateral sclerosis.

ABSTRACT: Microglial-derived inflammation has been linked to a broad range of neurodegenerative and neuropsychiatric conditions, including amyotrophic lateral sclerosis (ALS). Using single-cell RNA sequencing, a class of Disease-Associated Microglia (DAMs) have been characterized in neurodegeneration. However, the DAM phenotype alone is insufficient to explain the functional complexity of microglia, particularly with regard to regulating inflammation that is a hallmark of many neurodegenerative diseases. Here, we identify a subclass of microglia in mouse models of ALS which we term RIPK1-Regulated Inflammatory Microglia (RRIMs). RRIMs show significant up-regulation of classical proinflammatory pathways, including increased levels of Tnf and Il1b RNA and protein. We find that RRIMs are highly regulated by TNFα signaling and that the prevalence of these microglia can be suppressed by inhibiting receptor-interacting protein kinase 1 (RIPK1) activity downstream of the TNF receptor 1. These findings help to elucidate a mechanism by which RIPK1 kinase inhibition has been shown to provide therapeutic benefit in mouse models of ALS and may provide an additional biomarker for analysis in ongoing phase 2 clinical trials of RIPK1 inhibitors in ALS.

SUBMITTER: Mifflin L

PROVIDER: S-EPMC8020785 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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A RIPK1-regulated inflammatory microglial state in amyotrophic lateral sclerosis.

Mifflin Lauren L Hu Zhirui Z Dufort Connor C Hession Cynthia C CC Walker Alec J AJ Niu Kongyan K Zhu Hong H Liu Nan N Liu Jun S JS Levin Joshua Z JZ Stevens Beth B Yuan Junying J Zou Chengyu C

Proceedings of the National Academy of Sciences of the United States of America 20210301 13

Microglial-derived inflammation has been linked to a broad range of neurodegenerative and neuropsychiatric conditions, including amyotrophic lateral sclerosis (ALS). Using single-cell RNA sequencing, a class of Disease-Associated Microglia (DAMs) have been characterized in neurodegeneration. However, the DAM phenotype alone is insufficient to explain the functional complexity of microglia, particularly with regard to regulating inflammation that is a hallmark of many neurodegenerative diseases. ...[more]

PMID: 33766915

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Project description:Amyotrophic lateral sclerosis is a devastating disease characterized primarily by motor system degeneration, with clinical evidence of cognitive and behavioural change in up to 50% of cases. Amyotrophic lateral sclerosis is both clinically and biologically heterogeneous. Subgrouping is currently undertaken using clinical parameters, such as site of symptom onset (bulbar or spinal), burden of disease (based on the modified El Escorial Research Criteria) and genomics in those with familial disease. However, with the exception of genomics, these subcategories do not take into account underlying disease pathobiology, and are not fully predictive of disease course or prognosis. Recently, we have shown that resting-state EEG can reliably and quantitatively capture abnormal patterns of motor and cognitive network disruption in amyotrophic lateral sclerosis. These network disruptions have been identified across multiple frequency bands, and using measures of neural activity (spectral power) and connectivity (comodulation of activity by amplitude envelope correlation and synchrony by imaginary coherence) on source-localized brain oscillations from high-density EEG. Using data-driven methods (similarity network fusion and spectral clustering), we have now undertaken a clustering analysis to identify disease subphenotypes and to determine whether different patterns of disruption are predictive of disease outcome. We show that amyotrophic lateral sclerosis patients (n = 95) can be subgrouped into four phenotypes with distinct neurophysiological profiles. These clusters are characterized by varying degrees of disruption in the somatomotor (α-band synchrony), frontotemporal (β-band neural activity and γl-band synchrony) and frontoparietal (γl-band comodulation) networks, which reliably correlate with distinct clinical profiles and different disease trajectories. Using an in-depth stability analysis, we show that these clusters are statistically reproducible and robust, remain stable after reassessment using a follow-up EEG session, and continue to predict the clinical trajectory and disease outcome. Our data demonstrate that novel phenotyping using neuroelectric signal analysis can distinguish disease subtypes based exclusively on different patterns of network disturbances. These patterns may reflect underlying disease neurobiology. The identification of amyotrophic lateral sclerosis subtypes based on profiles of differential impairment in neuronal networks has clear potential in future stratification for clinical trials. Advanced network profiling in amyotrophic lateral sclerosis can also underpin new therapeutic strategies that are based on principles of neurobiology and designed to modulate network disruption.

Dataset Information

A RIPK1-regulated inflammatory microglial state in amyotrophic lateral sclerosis.

Publications

A RIPK1-regulated inflammatory microglial state in amyotrophic lateral sclerosis.

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