Abstract Endogenous homeostatic mechanisms can restore normal neuronal function following cocaine-induced neuroadaptations. Such mechanisms may be exploited to develop novel therapies for cocaine addiction, but a molecular target has not yet been identified. Here we profiled mouse gene expression during early and late cocaine abstinence to identify putative regulators of neural homeostasis. Cocaine activated the transcription factor, Nr4a1 , and its target gene, Cartpt , a key molecule involved in dopamine metabolism. Sustained activation of Cartpt at late abstinence was coupled with depletion of the repressive histone modification, H3K27me3, and enrichment of activating marks, H3K27ac and H3K4me3. Using both CRISPR-mediated and small molecule Nr4a1 activation, we demonstrated the direct causal role of Nr4a1 in sustained activation of Cartpt and in attenuation of cocaine-evoked behavior. Our findings provide evidence that targeting abstinence-induced homeostatic gene expression is a potential therapeutic target in cocaine addiction.
This is Data for "Upper cortical layer-driven network impairment in schizophrenia" paper, by Batiuk, Tyler et al., 2022 This repository contains: Supplementary Dataset Tables 1-4 (Supplementary_Dataset_Tables_1-4.xlsx). They contain DE genes and GO terms from snRNA-seq and visium analysis. Single nuclei and Visium spatial transcriptomics sequencing data (snRNA-seq_and_spatial_transcriptomics.zip) containing raw count matrices of snRNA-seq samples; Conos object with aligned snRNA-seq samples; snRNA-seq single nuclei cell subtype annotations; raw count matrices of Visium spatial transcriptomics samples; Visium spatial transcriptomics manual histological cortical layer annotations; and 10x Genomics spaceranger count pipeline output for Visium spatial transcriptomics data. Histological images of H&E stained Visium spatial transcriptomics samples mounted on visium slide capture area (visium_sample_images.zip)
Plant meristems, like animal stem cell niches, maintain a pool of multipotent, undifferentiated cells that divide and differentiate to give rise to organs. In Arabidopsis (Arabidopsis thaliana), the carpel margin meristem is a vital meristematic structure that generates ovules from the medial domain of the gynoecium, the female floral reproductive structure. The molecular mechanisms that specify this meristematic region and regulate its organogenic potential are poorly understood. Here, we present a novel approach to analyze the transcriptional signature of the medial domain of the Arabidopsis gynoecium, highlighting the developmental stages that immediately proceed ovule initiation, the earliest stages of seed development. Using a floral synchronization system and a SHATTERPROOF2 (SHP2) domain-specific reporter, paired with FACS and RNA sequencing, we assayed the transcriptome of the gynoecial medial domain with temporal and spatial precision. This analysis reveals a set of genes that are differentially expressed within the SHP2 expression domain, including genes that have been shown previously to function during the development of medial domain-derived structures, including the ovules, thus validating our approach. Global analyses of the transcriptomic data set indicate a similarity of the pSHP2-expressing cell population to previously characterized meristematic domains, further supporting the meristematic nature of this gynoecial tissue. Our method identifies additional genes including novel isoforms, cis-natural antisense transcripts, and a previously unrecognized member of the REPRODUCTIVE MERISTEM family of transcriptional regulators that are potential novel regulators of medial domain development. This data set provides genome-wide transcriptional insight into the development of the carpel margin meristem in Arabidopsis.
We report the results of a genome-wide analysis of transcription in Arabidopsis thaliana after treatment with Pseudomonas syringae pathovar tomato. Our time course RNA-Seq experiment uses over 500 million read pairs to provide a detailed characterization of the response to infection in both susceptible and resistant hosts. The set of observed differentially expressed genes is consistent with previous studies, confirming and extending existing findings about genes likely to play an important role in the defense response to Pseudomonas syringae. The high coverage of the Arabidopsis transcriptome resulted in the discovery of a surprisingly large number of alternative splicing (AS) events--more than 44% of multi-exon genes showed evidence for novel AS in at least one of the probed conditions. This demonstrates that the Arabidopsis transcriptome annotation is still highly incomplete, and that AS events are more abundant than expected. To further refine our predictions, we identified genes with statistically significant changes in the ratios of alternative isoforms between treatments. This set includes several genes previously known to be alternatively spliced or expressed during the defense response, and it may serve as a pool of candidate genes for regulated alternative splicing with possible biological relevance for the defense response against invasive pathogens.
Single-cell RNA sequencing (scRNA-seq) is a powerful tool to profile the transcriptomes of a large number of individual cells at a high resolution. These data usually contain measurements of gene expression for many genes in thousands or tens of thousands of cells, though some datasets now reach the million-cell mark. Projecting high-dimensional scRNA-seq data into a low dimensional space aids downstream analysis and data visualization. Many recent preprints accomplish this using variational autoencoders (VAE), generative models that learn underlying structure of data by compress it into a constrained, low dimensional space. The low dimensional spaces generated by VAEs have revealed complex patterns and novel biological signals from large-scale gene expression data and drug response predictions. Here, we evaluate a simple VAE approach for gene expression data, Tybalt, by training and measuring its performance on sets of simulated scRNA-seq data. We find a number of counter-intuitive performance features: i.e., deeper neural networks can struggle when datasets contain more observations under some parameter configurations. We show that these methods are highly sensitive to parameter tuning: when tuned, the performance of the Tybalt model, which was not optimized for scRNA-seq data, outperforms other popular dimension reduction approaches – PCA, ZIFA, UMAP and t-SNE. On the other hand, without tuning performance can also be remarkably poor on the same data. Our results should discourage authors and reviewers from relying on self-reported performance comparisons to evaluate the relative value of contributions in this area at this time. Instead, we recommend that attempts to compare or benchmark autoencoder methods for scRNA-seq data be performed by disinterested third parties or by methods developers only on unseen benchmark data that are provided to all participants simultaneously because the potential for performance differences due to unequal parameter tuning is so high.
Morganella morganii belongs to the tribe Proteeae of the Enterobacteriaceae family. This species is considered as an unusual opportunistic pathogen that mainly causes post-operative wound and urinary tract infections. However, certain clinical M. morganii isolates present resistance to multiple antibiotics by carrying various resistant genes (such as blaNDM-1, and qnrD1), thereby posing a serious challenge for clinical infection control. Moreover, virulence evolution makes M. morganii an important pathogen. Accumulated data have demonstrated that M. morganii can cause various infections, such as sepsis, abscess, purple urine bag syndrome, chorioamnionitis, and cellulitis. This bacterium often results in a high mortality rate in patients with some infections. M. morganii is considered as a non-negligent opportunistic pathogen because of the increased levels of resistance and virulence. In this review, we summarized the epidemiology of M. morganii, particularly on its resistance profile and resistant genes, as well as the disease spectrum and risk factors for its infection.
