Cortex Command 27 Rus Skachat

The human capacity to infer 3D structure from a sequence of images is limited because of the challenges posed for understanding the topological layout of the brain using a series of 2D sections. The cerebral cortex, for example, is a highly convoluted structure whose many folds result in nearby structures on a section to be located far away from one another in cortical space. It is thus often difficult to gain a perspective on the tiling of the cortical sheet by different functional areas by comparison with a conventional atlas. This problem is more severe when the angle of sectioning of a brain specimen (or block) does not precisely match the angle of sectioning in the reference atlas. To address these issues, investigators have previously taken the approach of physically flattening brain specimens, after first separating the cortical mantle from the underlying white matter (Olavarria and Van Sluyters 1985; Tootell and Silverman 1985; Sincich et al. 2003). More modern approaches allow for the virtual extraction of cortical surfaces and volumetric brain components from digitized brain scans, which one can then manipulate and analyze on a computer (Drury et al. 1996; Van Essen et al. 2001).

cortex command 27 rus skachat

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You can execute these commands from the Cortex XSOAR CLI, as part of an automation, or in a playbook. After you successfully execute a command, a DBot message appears in the War Room with the command details.

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We show transcriptional, histone modification (H3K27ac) and DNA methylation changes in genes related to the immune and neuronal system, potentially relevant to neuroinflammatory and cognitive symptoms of GWI. Further evidence suggests altered proportions of myelinating oligodendrocytes in the frontal cortex, perhaps connected to white matter deficits seen in GWI sufferers.

Trimmed files were aligned with the STAR aligner [43] (version 2.5.2a) in a two-pass mode. The GENCODE GRCm38.p4 assembly (mm10) and annotations were obtained from the GENCODE website [44, 45] and used throughout. For the frontal cortex, there was an average of 33,547,161 reads per sample and 98.2% average mapped reads. For the hippocampus, there was an average of 35,429,647 reads per sample and 98.2% average mapped reads.

The R Bioconductor package DeconRNASeq [51] was used to estimate the proportion of different cell types within the sample from the RNA-seq data. Data enriched for specific CNS cell types were downloaded from the Gene Expression Omnibus (GEO) [52, 53], Series GSE52564, which contains data from the Mus musculus cerebral cortex [54], to use as a reference of cell-type-specific gene expression. This RNA-seq data was trimmed and aligned and gene expression quantified as above. An expression signature for each of six cell types (astrocytes, neurons, oligodendrocyte precursor cells (OPC), myelinating oligodendrocytes (MO), microglia and endothelial cells) was obtained by finding those genes with a five-fold difference in expression in one cell type, compared to each of the others.

Samples were clustered using a Poisson dissimilarity matrix to determine if samples from the same exposure group showed similar expression profiles. As can be seen in Additional file 1: Figure S1 and Additional file 2: Figure S2, the samples largely clustered by exposure group. The only sample that appeared to be out of place was a CORT + DFP sample in the frontal cortex that appeared intermediate between CORT and DFP alone.

In the frontal cortex, the RNA-seq analysis identified 206 GENCODE genes (204 with unique entrez IDs) that were uniquely differentially expressed in the CORT + DFP exposure group compared to all other groups (Additional file 3: Table S1). Enrichment analysis showed 12 enriched KEGG pathways (Additional file 4: Figure S3; Additional file 5: Table S2) and 24 enriched GO BP annotations (Fig. 2; Additional file 6: Table S3). These annotations formed several broad groups related to immune response, including chemokine production, oxidative stress and steroid biosynthesis.

Frontal cortex RNA-seq significantly enriched gene ontology biological process annotations. Gene ontology biological process annotations significantly enriched in genes which were differentially expressed in the frontal cortex of CORT + DFP exposed mice, with groups of similar annotations highlighted

In the hippocampus, 667 GENCODE genes (637 with unique entrez IDs) were uniquely differentially expressed in the CORT + DFP exposure group (Additional file 7: Table S4) compared to all other groups. Enrichment analysis showed 19 enriched KEGG pathways (Additional file 8: Figure S4, Additional file 9: Table S5) and 294 enriched GO BP annotations (Fig. 3, Additional file 10: Table S6). Similar to the frontal cortex, these annotations were grouped into several clusters (Fig. 3), including immune-related annotations (e.g. I-kappaB and NF-kappaB signalling), annotations related to nervous system differentiation, and development.

An interesting incidental finding in the cortex was that CORT exposure, with or without co-exposure with DFP, was associated with an increase in the proportion of neurons and a decrease in the proportion of myelinating oligodendrocytes (MOs) in the frontal cortex (Fig. 4b). As we would not expect neurogenesis to occur in the frontal cortex, this suggests that the increase in the proportion of neurons is driven by a decrease in the absolute number of myelinating oligodendrocytes. A reduced number of oligodendrocytes would be in line with previous work in rats where CORT was shown to reduce the proliferation of oligodendrocytes [78, 79]. We emphasize that these are estimated cell proportions; however, the results indicate that stereology to confirm this will be important in future studies.

Frontal cortex H3K27ac ChIP-seq significantly enriched gene ontology biological process annotations. Top 50 GO BP annotations significantly enriched for differential enrichment of H3K27ac with CORT + DFP exposure

These findings demonstrate that there are potential changes in neuronal-related gene expression in the frontal cortex, as was also seen in the hippocampus RNA-seq, highlighted by the fact that 33 genes were found in both the ChIP-seq frontal cortex analysis and the hippocampus RNA-seq analysis.

As shown in Fig. 6, there is not a large overlap in genes found between any of our analyses. However, this disparity may be partly explained from the aforementioned difference between mRNA and DNA, whereby one locus can produce many mRNA molecules, but DNA either has a modification or does not. This is reflected by the fact that the largest percentage overlap is between those genes found with RRBS and ChIP-seq, as these are both examining DNA modifications: 12% of genes found in frontal cortex RRBS, and 16% in hippocampus RRBS, are also found in the frontal cortex ChIP-seq, whereas this is only 1% and 5% for frontal cortex and hippocampus RNA-seq respectively. Similarly, 15.5% of genes found in the frontal cortex RNA-seq are also found in the hippocampus RNA-seq.

Annotated GENCODE genes found in each of our differential analyses. UpSetR diagram [73] of annotated GENCODE genes found in each of our differential analyses: frontal cortex RRBS (FC RRBS), frontal cortex H3K27ac ChIP-seq (FC ChIP), hippocampus RNA-seq (Hipp RNA), frontal cortex RNA-seq (FC RNA) and hippocampus RRBS (Hipp RRBS)

Overall, our results represent several interesting findings. First, as expected, there was a large change in the expression of immune-related genes in both the frontal cortex and hippocampus, building upon previous findings in this model [4]. Second, many genes associated with synaptic function are changed in their activity, as shown by our frontal cortex H3K27ac ChIP-seq data and hippocampus RNA-seq. These changes in gene expression seem to be subtler than those found for immune-related genes (lower expression), but differential expression of genes related to synaptic function in mice is associated with impaired memory and cognition, consistent with impairments reported by GWI suffers. Interestingly, long-term potentiation- and depression-related genes are enriched in the ChIP-seq data. Finally, we see evidence of not just a change in gene activity but of a suggested change in cell proportions. This is in line with previous work with CORT [78, 79].

It is possible that microglia are responsible for this large change in immune-related gene expression, but we acknowledge that other cells, such as astrocytes, also express cytokines and chemokines (e.g. Kim et al. [80]). However, this is usually at a much lower level than that in microglia: in genes significantly differentially expressed uniquely with CORT + DFP in both the frontal cortex and hippocampus the six genes with the largest fold change are expressed in microglia (Additional file 26: Table S18). Therefore, although other cell types may be contributing to cytokine and chemokine expression, it is highly unlikely that microglia are not the cell type driving this change. Similarly, we attribute many of the transmembrane transporter-related annotations we see in the frontal cortex ChIP-seq data and hippocampus RNA-seq data to neurons; however, many of these transporters are also expressed in glial cells [54]. For this reason, future work should be carried out to isolate or enrich specific cell populations, allowing these predictions to be tested.

In relation to the potential reduction in myelinating oligodendrocytes in the cortex, this may have an effect on some of the phenotypes seen in GWI: reduced oligodendrocytes have been linked to major depressive disorder (MDD), functional consequences in neurons and mood-related symptoms in rats [87]. As this change in cell proportion would affect myelinating cells, it could also contribute to the reported alterations in white matter in GWI veterans [20, 88,89,90]. 041b061a72