bioRxiv Science⌕ Search

bioRxiv · 10.1101/2022.06.13.496004

Methamphetamine induced regional-specific transcriptomic and epigenetic changes in the rat brain

Abstract

BackgroundMethamphetamine (METH) is a highly addictive central nervous system stimulant. Chronic use of METH is associated with multiple neurological and psychiatric disorders. An overdose of METH can cause brain damage and even death. Mounting evidence indicates that epigenetic changes and functional impairment in the brain occur due to addictive drug exposures. However, the responses of different brain regions to a METH overdose remain unclear. ResultsWe investigated the transcriptomic and epigenetic responses to a METH overdose in four regions of the rat brain, including the nucleus accumbens, dentate gyrus, Ammons horn, and subventricular zone. We found that 24 hours after METH overdose, 15.6% of genes showed changes in expression and 27.6% of open chromatin regions exhibited altered chromatin accessibility in all four rat brain regions. Interestingly, only a few of those differentially expressed genes and differentially accessible regions were affected simultaneously. Among four rat brain regions analyzed, 149 transcription factors and 31 epigenetic factors were significantly affected by METH overdose. METH overdose also resulted in opposite-direction changes in regulation patterns of both gene and chromatin accessibility between the dentate gyrus and Ammons horn. Approximately 70% of chromatin-accessible regions with METH-induced alterations in the rat brain are conserved at the sequence level in the human genome, and they are highly enriched in neurological processes. Many of these conserved regions are active brain-specific enhancers and harbor SNPs associated with human neurological functions and diseases. ConclusionOur results indicate strong region-specific transcriptomic and epigenetic responses to a METH overdose in distinct rat brain regions. We describe the conservation of region-specific gene regulatory networks associated with METH overdose. Overall, our study provides clues toward a better understanding of the molecular responses to METH overdose in the human brain.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Miao, B., Xing, X., Bazylianska, V., Madden, P., Moszczynska, A., Zhang, B.. 2022-06-17. Methamphetamine induced regional-specific transcriptomic and epigenetic changes in the rat brain. https://doi.org/10.1101/2022.06.13.496004

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗