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Saffron, A.

Publications and source records attributed to Saffron, A..

3 recordsLinked to original sources

A Meta-Analysis of the Effects of Chronic Stress on the Prefrontal Transcriptome in Animal Models and Convergence with Existing Human Data

BackgroundChronic stress is a major risk factor for psychiatric disorders, including anxiety, depression, and post-traumatic stress disorder. Chronic stress can cause structural alterations like grey matter atrophy in key emotion-related areas such as the prefrontal cortex (PFC). To identify biological pathways affected by chronic stress in the PFC, researchers have performed transcriptional profiling (RNA-sequencing, microarray) to measure gene expression in rodent models. However, transcriptional signatures in the PFC that are shared across different chronic stress paradigms and laboratories remain relatively unexplored. MethodsWe performed a meta-analysis of publicly available transcriptional profiling datasets within the Gemma database. We identified six datasets that characterized the effects of either chronic social defeat stress (CSDS) or chronic unpredictable mild stress (CUMS) on gene expression in the PFC in mice (n=117). We fit a random effects meta-analysis model to the chronic stress effect sizes (log(2) fold changes) for each transcript (n=21,379) measured in most datasets. We then compared our results with two other published chronic stress meta-analyses, as well as transcriptional signatures associated with psychiatric disorders. ResultsWe identified 133 genes that were consistently differentially expressed across chronic stress studies and paradigms (false discovery rate (FDR)<0.05). Fast Gene Set Enrichment Analysis (fGSEA) revealed 53 gene sets enriched with differential expression (FDR<0.05), dominated by glial and neurovascular markers (e.g., oligodendrocyte, astrocyte, endothelial/vascular) and stress-related signatures (e.g., major depressive disorder, hormonal responses). Immediate-early gene markers of neuronal activity (Fos, Junb, Arc, Dusp1) were consistently suppressed. Many of the identified effects resembled those seen in previous meta-analyses characterizing stress effects (CSDS, early life stress), despite minimal overlap in included samples. Moreover, some effects resembled previous observations from psychiatric disorders, including alcohol abuse disorder, major depressive disorder, bipolar disorder, and schizophrenia. ConclusionOur study demonstrates that chronic stress induces a robust, cross-paradigm PFC signature characterized by down-regulation of glia/myelin and vascular pathways and suppression of immediate-early gene activity, highlighting cellular processes linking chronic stress exposure, PFC dysfunction, and psychiatric disorders. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=92 SRC="FIGDIR/small/683091v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1062c5eorg.highwire.dtl.DTLVardef@4ae148org.highwire.dtl.DTLVardef@c747corg.highwire.dtl.DTLVardef@1b3a713_HPS_FORMAT_FIGEXP M_FIG C_FIG Key PointsO_LIChronic stress is linked to many human psychiatric disorders involving the prefrontal cortex (PFC). C_LIO_LIMeta-analysis showed that stressed mice had less gene expression for glia and neural activity in the PFC. C_LIO_LIPFC gene expression in chronically stressed mice mirrors patterns seen in psychiatric patients. C_LI Plain Language SummaryChronic stress has been shown to have lasting effects on the brain, contributing to cognitive impairments and the risk of psychiatric disorders such as anxiety, depression, and post-traumatic stress disorder. The prefrontal cortex, a brain region that is important for behavioral and attentional control, is sensitive to chronic stress. We combined information from six public mouse datasets to identify consistent effects of chronic stress on gene expression (mRNA) in the prefrontal cortex. In these studies, mice that had experienced chronic stress showed a decreased amount of mRNA related to a variety of non-neuronal support cells (glia) and blood vessels, and general neural activity. The mouse chronic stress signature overlapped with gene-activity patterns reported in human psychiatric conditions, suggesting a biological bridge between chronic stress and the onset of psychopathology.

neuroscience↗

Optimized R2 Retroelement Complexes Enable Precise and Efficient DNA Insertion into Plant Genomes

Precise, targeted insertion of multi-kilobase DNA sequences into plant genomes is critical for studying gene function, ensuring robust transgene expression, and stacking traits in crops, but remains challenging. Existing targeted insertion methods in plants relying on programmable nucleases are inefficient and can generate unwanted mutations. Newer technologies based on prime editors, transposases, and site-specific recombinases extend capabilities but remain constrained with low efficiencies, off-target integration, silencing, or limited DNA payload size. R2 non-long terminal repeat (non-LTR) retrotransposons integrate via target-primed reverse transcription (TPRT) specifically targeting the 25S ribosomal DNA multicopy site and enabling double-strand-break-free installation of gene-sized DNA sequences. We adapted the avian Taeniopygia guttata R2 protein (R2Tg) for targeted DNA insertion into plant genomes through engineering of R2Tg expression cassettes and RNA payloads carrying intron-disrupted mCherry and RUBY retrotransposition reporters with length-optimized rDNA homology arms. These efforts, together with optimized construct delivery formats and incubation temperatures, define R2 editor design rules enabling efficient DNA integration and functional protein expression from the 25S rDNA locus. In Nicotiana benthamiana leaves, Arabidopsis thaliana protoplasts, and Solanum lycopersicum seedlings, the optimized R2Tg editor system achieved targeted insertion with efficiencies up to 24% payloads ranging in size from 2kb to 5kb. This work establishes a compact R2Tg ribonucleoprotein platform for targeted DNA insertion into plant genomes, targeting a multicopy genomic safe harbor site to enable efficient multi-kilobase gene addition

bioengineering↗

Short Report: A Meta-Analysis of the Effects of Sleep Deprivation on the Cortical Transcriptome in Animal Models

Sleep deprivation (SD) causes large disturbances in mood and cognition. The molecular basis for these effects can be explored using transcriptional profiling to quantify brain gene expression. In this report, we used a meta-analysis of public transcriptional profiling data to discover SD effects on gene expression that are consistent across studies and paradigms. To conduct the meta-analysis, we used pre-specified search terms related to rodent SD paradigms to identify relevant studies within Gemma, a database containing >19,000 re-analyzed microarray and RNA-Seq datasets. Eight studies met our systematic inclusion/exclusion criteria. These studies characterized the effect of 18 SD interventions on gene expression in the mouse cerebral cortex (collective n=293). For each gene with sufficient data (n=16,290), we fit a random effects meta-analysis model to the SD effect sizes (log(2) fold changes). Our meta-analysis revealed 182 differentially expressed genes in response to SD (false discovery rate: FDR<0.05), most of which (115/182) showed similar effects (FDR<0.05) in an independent large dataset (GSE114845: n=86 RNA-Seq samples from n=222 mice). Gene-set enrichment analysis revealed down-regulation in pathways related to stress response (e.g., glucocorticoid receptor Nr3c1), vasculature, growth and development, and upregulation related to stress, inflammation, and neuropeptide signalling. Exploratory analyses suggested that recovery sleep (included in six contrasts: range: 1-18 hrs), could reverse the impact of SD on gene expression. Our meta-analysis provides a useful reference database illustrating the diverse molecular impact of SD on the rodent cerebral cortex. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/648791v2_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@52984dorg.highwire.dtl.DTLVardef@8d1174org.highwire.dtl.DTLVardef@174ed85org.highwire.dtl.DTLVardef@195e8bf_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

neuroscience↗