Project description:Abstract: Noncoding variants of presumed regulatory function contribute to neuropsychiatric disease heritability. 4442 noncoding variants connected to risk for 10 neuropsychiatric disorders, including autism spectrum disorder, attention deficit hyperactivity disorder, bipolar disorder, borderline personality disorder, major depression, generalized anxiety disorder, panic disorder, post-traumatic stress disorder, obsessive-compulsive disorder, and schizophrenia, were studied within developing human neural cells. Integrating epigenomic and transcriptomic data with massively parallel reporter assays identified 637 disease-associated single-nucleotide variants (daSNVs) with altered transcription-regulating activity. Expression-gene mapping, network analyses, and chromatin looping identified 619 candidate disease-relevant target genes modulated by these daSNVs. Specific daSNV-associated molecular pathways mapped to prevalent mental health symptoms. These data provide a framework resource to study inherited pathogenic risk for human neuropsychiatric disorders. Purpose: To place disease-associated single nucleotide variants (daSNVs) in genomic context within human neural cells, matched RNA-seq, chromatin accessibility profiling via ATAC-seq, and enhancer-promoter looping via H3K27ac HiChIP was performed in ES cells and at day 2 (N-D2), 10 (N-D10), and 28 (N-D28) of neuronal differentiation. Additionally, anterior (A-NPC) and posterior (P-NPC) neuronal stem cells were generated and studied. Matched sequencing was also performed on primary human astrocytes from adult brains. Processed data for all cell types can be found in this repository. Raw data D10 and D28 RNA seq samples can be found here. Raw data for D0, D2, A-NSC, A-D2, P-NSC, and P-D2 data can be found here: https://www.biorxiv.org/content/10.1101/2020.12.02.398677v1.abstract. Raw data for Astrocytes can be found with the Donohue, et al. "The combinatorial cis-regulatory vocabulary of cell type-specific gene regulation in homeostasis and disease".

Project description:In rice, gene expression profiling under different stresses have been carried out earlier through microarrays and SAGE (Kawasaki et al., 2001; Rabbani et al., 2003,;. Yazaki et al., 2003, 2004; Walia et al., 2005; Zhou et al., 2005; Jiao et al., 2005; Zhou, 2007; Wang, et al., 2007)). The results reported so far were mostly derived from different stress treatments, array formats, species, tissue types, and time courses, which make it difficult for direct comparison among studies. Also, most of the stress experiments on expression profiles were conducted under laboratory, growth chamber or field grown plants subjected to stress in laboratory conditions, usually by inducing stress with exogenous compounds such as abscisic acid (ABA) or gibberellin (GA) genes (Yazaki et al., 2003, 2004; Rabbani et al., 2003), salt (Kawasaki et al., 2001; Rabbani et al., 2003; Walia et al., 2005), organ specific gene expression under high salinity (Jiao et al., 2005) and drought (Zhou et al., 2007), comparison with different cultivars (Wang et al., 2007) and other environmental stresses such as cold and drought (Rabbani et al., 2003; Zhou et al., 2005). However, such laboratory, growth chamber grown plants or field grown plants subjected to stress under laboratory conditions significantly differ from the field stress conditions and therefore the resultant plant response may not truly represent the actual stress conditions prevailing on field. This has been extensively reviewed by Mittler, 2006 and the need for utilizing data from multiple stress experiments for developing plants resistant to stress has been focused. The review also focuses on evaluating farmers experiences in stress management and kinds of stress that a plant is subjected prior to taking up research on stress resistances has been of significant importance. In the present study, to bridge the gap between the available data on drought stress and the gene regulation under field drought conditions has been paid attention. The outcome of this data will give new leads for breeders to design strategies to develop plants resistant to drought without much loss in yield. In the present study experiments were conducted under different field capacities using a rainout shelter to minimizing the effects of drought induction due to rains. The microarray analysis is thus expected to throw light on stress responsive gene expression in field drought conditions. Till now, to our knowledge there have been no reports on gene expression in rice under field drought conditions. Further, there is no data available on gene expression profiling during various stages of plant growth in leaf and panicle tissues under different levels of water stress. Here, we have focused on generating stress responsive gene (SRG) expression profiles in field drought conditions through the entire life cycle of rice plant. Optimal drought simulation was achieved by conducting a drought stress experiment and validated by a series of dry runs. The main reason for evaluating gene expression under drought in field condition is as drought itself is not limited to water stress but various other factors, such as heat, humidity, hot winds, and long day lengths effect the gene regulation. All these are likely to trigger common pathways that are also a part of drought stress response and therefore significantly affect the expression profiles. In the present study, field drought stress was initiated from 31 days after germination and transplantation to the field at 100%, 60%, 40% and 15% field capacities designated as A1, A2, A3, and A4. It took 7 days for the onset of the actual drought for the experimental plots to reach to their designated field capacities from 100% FC. Samples were collected from 7 days after drought stress to 33 days after drought stress to evaluate different drought stress regimes based on the developmental stages of the plant. At all the tested stress levels the data points were Hierarchically grouped and clustered sets of genes were examined and an approximate number of clusters to be formed were estimated. Finding a group of genes showing a similar pattern of gene expression by means of expression data from microarray analysis is the basis for identification of genes and pathways controlling a specific trait. We have followed unsupervised classification and clustered the genes hierarchically after normalizing expression data to obtain a set of co-regulated genes. Analysis of data revealed that there are at least 3 to 4 clusters which show differential regulation under each stress regime. The annotated genes falling in these clusters interestingly include a number of novel genes from N22 library along with some known candidate genes for drought tolerance which were earlier identified in our study (Markandeya et al., 2007). Expression pattern of these genes from 7 days after drought stress to 33 days after drought stress with their fold changes were evaluated in the present study. In particular, it is observed that many genes of unknown function are also co-regulated in an interesting pattern along with known genes.

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Genome-wide analysis of gene expression in wild-type and Sp4 hypomorphic mouse cerebellum

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