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Biology subjects

Groves, P.

Publications and source records attributed to Groves, P..

2 recordsLinked to original sources

Glucocorticoid receptors mediate reprogramming of astrocytes in depression.

Psychiatric disorders are among the most pressing problems of the modern society, with various forms of depression affecting more than 300 millions of people worldwide. Dysfunction of glial cells has consistently been reported in major depressive disorder (MDD); however, no comprehensive resource detailing glial dysfunction is available. To provide insight into neurobiological mechanisms behind severe psychiatric symptoms, we performed transcriptional analysis of post-mortem samples from a subpopulation of suicide completers with previously reported glial abnormalities. We focused on BA25, a subregion of the prefrontal cortex prioritized for targeted medical interventions, due to its metabolic aberrations in disease. We found that a significant portion of genes deregulated in MDD is enriched in glia, with astrocyte-specific genes representing the highest fraction. Then we employed a novel protocol for enriching astrocytic nuclei to provide a detailed molecular signature of astrocytes in MDD. The analysis of the gene set revealed the glucocorticoid receptor (GR) as a key regulatory transcription factor. We found that astrocyte-specific elimination of the GR in mice largely prevented transcriptional, metabolic and behavioral changes elicited by chronic stress. We also demonstrated that regional manipulation of glutamate turnover in astrocytes suffices to elicit discrete traits of depressive-like behavior. Our data points to astrocytes as a key cellular site of convergence of multiple traits of depression and provide a resource for exploring novel targets for glia-focused therapeutic approaches.

neuroscience↗

Knowledge-guided analysis of ‘omics’ data using the KnowEnG cloud platform

We present KnowEnG, a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis and expression signature analysis. The system offers knowledge-guided data-mining and machine learning algorithms, where user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge-bases and encoded in a massive Knowledge Network. KnowEnG adheres to FAIR principles: its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution of compute-intensive and data-intensive algorithms, and are interoperable with other computing platforms. They are made available through multiple access modes including a web-portal, and include specialized visualization modules. We present use cases and re-analysis of published cancer data sets using KnowEnG tools and demonstrate its potential value in democratization of advanced tools for the modern genomics era.

cancer biology↗