Genetic cooperativity in multi-layer networks implicates cell survival and senescence in the striatum of Huntington’s disease mice synchronous to symptoms

Abstract

Motivation Huntington’s disease (HD) may evolve through gene deregulation. However, the impact of gene deregulation on the dynamics of genetic cooperativity in HD remains poorly understood. Here, we built a multi-layer network model of temporal dynamics of genetic cooperativity in the brain of HD knock-in mice (allelic series of Hdh mice). To enhance biological precision and gene prioritization, we integrated three complementary families of source networks, all inferred from the same RNA-seq time series data in Hdh mice, into weighted-edge networks where an edge recapitulates path-length variation across source-networks and age-points. Results Weighted edge networks identify two consecutive waves of tight genetic cooperativity enriched in deregulated genes (critical phases), pre-symptomatically in the cortex, implicating neurotransmission, and symptomatically in the striatum, implicating cell survival (e.g. Hipk4) intertwined with cell proliferation (e.g. Scn4b) and cellular senescence (e.g. Cdkn2a products) responses. Top striatal weighted edges are enriched in modulators of defective behavior in invertebrate models of HD pathogenesis, validating their relevance to neuronal dysfunction in vivo. Collectively, these findings reveal highly dynamic temporal features of genetic cooperativity in the brain of Hdh mice where a 2-step logic highlights the importance of cellular maintenance and senescence in the striatum of symptomatic mice, providing highly prioritized targets. Availability and implementation Weighted edge network analysis (WENA) data and source codes for performing spectral decomposition of the signal (SDS) and WENA analysis, both written using Python, are available at http://www.broca.inserm.fr/HD-WENA/. Supplementary information Supplementary data are available at Bioinformatics online.

Shape deformation analysis reveals the temporal dynamics of cell-type-specific homeostatic and pathogenic responses to mutant huntingtin

“…Interestingly, biological content analysis (see Materials and methods), including KEGG pathway and gene ontology (GO) analysis, revealed that, in addition to implicating the ‘HD pathway’ (in interneurons, astrocytes) and pathways involved in neuronal activity (e.g., ‘Wnt signaling’ and ‘cAMP signaling’ in Drd2 -MSNs), GDS clusters implicate stress response pathways in all cell types ( Supplementary file 2 ). Noticeably, cluster centroid Drd2-1 ( Drd2 -MSNs: cluster centroid 1, down-regulation across CAG repeats) implicates stress response and cell survival genes such as Hipk4 , a kinase that promotes HD pathogenesis in transgenic flies ( Al-Ramahi et al, 2018 ), and Arpp21 , a cAMP-regulated phosphoprotein that protects neurons against HD pathogenesis in transgenic Caenorhabditis elegans nematodes ( Bigan et al, 2020 ). Using weighted-edge network analysis of the whole-striatum time-series RNA-seq data, we previously found that these two genes belong to a dynamic network that implicates cell survival and cellular senescence and that forms in the striatum of Hdh mice as they become highly symptomatic on a behavioral level (i.e., gene nodes come together into short-path interactions at 10 months of age; Bigan et al, 2020 ), which, as defined herein, may primarily occur in Drd2 -MSNs.…”

“…Noticeably, cluster centroid Drd2-1 ( Drd2 -MSNs: cluster centroid 1, down-regulation across CAG repeats) implicates stress response and cell survival genes such as Hipk4 , a kinase that promotes HD pathogenesis in transgenic flies ( Al-Ramahi et al, 2018 ), and Arpp21 , a cAMP-regulated phosphoprotein that protects neurons against HD pathogenesis in transgenic Caenorhabditis elegans nematodes ( Bigan et al, 2020 ). Using weighted-edge network analysis of the whole-striatum time-series RNA-seq data, we previously found that these two genes belong to a dynamic network that implicates cell survival and cellular senescence and that forms in the striatum of Hdh mice as they become highly symptomatic on a behavioral level (i.e., gene nodes come together into short-path interactions at 10 months of age; Bigan et al, 2020 ), which, as defined herein, may primarily occur in Drd2 -MSNs. Cluster centroids Drd2-5 (up-regulation), Drd1-6 (up-regulation), Drd1-8 (down-regulation), Interneurons-0 (down-regulation), Interneurons-4, and Astrocytes-4 (up-regulation) implicate ‘DNA repair’ or ‘DNA damage response’, suggesting that DNA repair is a biological process that is significantly altered in HD models in all four cell types.…”

“…Precisely inferring how such biological processes are coordinated at a molecular systems level can be aided by the analysis of multidimensional datasets, for example, transcriptomic time-series data collected across genotypes or cell types. Such datasets are increasingly available to ND research ( Langfelder et al, 2016 ; Maniatis et al, 2019 ), offering opportunities to comprehensively probe how molecular systems may respond to disease drivers, noticeably by using network inference ( Langfelder et al, 2016 ; Bigan et al, 2020 ; Mégret et al, 2020 ). Probing molecular responses to disease drivers can greatly benefit from integrating transcriptomic data with functional screening data to discern pathogenic causative from compensatory responses.…”

Identification of Key Methylation Differentially Expressed Genes in Posterior Fossa Ependymoma Based on Epigenomic and Transcriptome Analysis

“…Sodium voltage-gated channel beta subunit 4 (SCN4B), one of the beta subunits of the sodium channel, regulates the channel gated dynamics and causes voltage-dependent negative shifts in the activation of some α-sodium channels [43] . The variants in SCN4B have been shown to be associated with ventricular tachyacrdia [44] , Huntington's disease [45] , and can be a new biomarker of aggressive cancers [45] . Moreover, Marin et al showed that SCN4B has a very high interconnection and participation in febrile seizures,nervous disorders, neuromuscular and neurodegenerative diseases, and neurobehavioral manifestations [47] .…”

HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data

“…As we argued above, systematic, automated validation of models is a critical prerequisite of collaborative model development. Accordingly, the BSP includes a software framework for quantitative model validation and testing that explicitly supports applying a given validation test to different models and storing the results [59]. The framework consists of a web service, and a set of test suites, which are Python modules based on the SciUnit package.…”