Pnmt Download Nec Driver
Gildo Santiago
Given the importance of the central nervous system in modulating stress responses, this project sought to investigate cellular Pnmt expression in the central nervous system using a genetic-marking strategy with a Pnmt-Cre-recombinase knock-in driver strain (Pnmt+/Cre) and a β-galactosidase (βGal) reporter strain (R26R+/βGal) in parallel with Pnmt-specific immunofluorescent histochemical staining to identify Pnmt+ cells in the adult mouse cerebellum, hypothalamus, and cerebral cortex. The results show extensive patterns of active and historical Pnmt protein expression throughout the cerebellum and hypothalamus, with significant neuropeptide Y co-expression in the hypothalamus and considerable historical Pnmt expression throughout the cerebral cortex.
Simultaneous interrogation of tumor genomes and transcriptomes is underway in unprecedented global efforts. Yet, despite the essential need to separate driver mutations modulating gene expression networks from transcriptionally inert passenger mutations, robust computational methods to ascertain the impact of individual mutations on transcriptional networks are underdeveloped. We introduce a novel computational framework, DriverNet, to identify likely driver mutations by virtue of their effect on mRNA expression networks. Application to four cancer datasets reveals the prevalence of rare candidate driver mutations associated with disrupted transcriptional networks and a simultaneous modulation of oncogenic and metabolic networks, induced by copy number co-modification of adjacent oncogenic and metabolic drivers. DriverNet is available on Bioconductor or at
Cancer genome sequencing experiments are designed to enumerate all somatic mutations within a cancer. Some of these mutations will serve as actionable genomic aberrations upon which to develop and apply targeted therapies (for example, mutations in PIK3CA, BRAF, and KRAS) and ultimately enabling rational frameworks for improved clinical management and patient care based on precise genomic patterns of somatic alteration. To this end, next generation sequencing (NGS) technology has shifted the rate-limiting step from identifying all cancer mutations in a sequenced genome to identifying the relatively few functional mutations that drive the phenotype of malignant cells. Therein lies a major challenge in the cancer genomics field: distinguishing pathogenic, driver mutations from the so-called passenger mutations that accrue stochastically, but do not confer selective advantages.
In order to discover novel driver mutations, several large-scale sequencing initiatives such as The Cancer Genome Atlas project (TCGA, for example, [1]) are generating simultaneous whole genome and transcriptome interrogations for hundreds of cases of the same tumor type. This opens the possibility of ascribing the impact of individual somatic mutations on gene expression networks. Initial observations in high-throughput datasets, coupled with innumerable functional studies suggest that driver mutations are expected to alter gene expression of their cognate proteins, their interacting partners, or genes that share the same biochemical pathway. This will lead to a correlated pattern of gene expression in a network of genes associated with a driver mutation, which differs from benign passenger mutations with little to no phenotype. Moreover, somatic aberrations in genes may alter more than one transcriptional network, thus enabling the enumeration of a group of pathways driven by a single genomic event. The importance of placing mutations in the context of their gene expression has been illuminated recently by Prahallad and colleagues [2], who established the therapeutic effect of PLX4032 against the BRAF V600E oncoprotein, which is mechanistically linked to the activation of EGFR. Thus, differential expression of EGFR in different cell types (colon cancers versus melanomas) has a dramatic impact on drug efficacy. Consequently, knowing active pathways coupled with mutational profiles will be critical for implementation of therapeutic decisions informed by the presence of mutations in a cancer.
We suggest that integrative analysis of genomic aberrations and transcriptional profiles in cancer will reveal somatic mutations that drive biological processes, regardless of the population frequency. Furthermore, we propose that biological networks can be leveraged to relate mutations to their consequent effect on transcription and gene expression. Figure 1A shows an example of high-level amplification of EGFR in a glioblastoma multiforme (GBM) tumor, accompanied by the coincident outlying expression of genes that are connected to EGFR through known biological pathways. We note that BRAF in this case, although not amplified itself, exhibits elevated expression compared to the population distribution. Other genes known to interact with EGFR exhibit similar extreme changes in expression levels in this example, such that PI3K signaling and MAPK signaling could be affected by this single genomic event. Figure 1B shows fitted Gaussian expression distributions of three genes that interact with EGFR: FGF11, PIK3R1, and PRKACB, and shows that some cases with outlying expression have coincident EGFR amplifications. Our assumption is that amplification of EGFR in these cases has driven expression of the example genes to the tails of their respective distributions. Thus, extreme changes in expression levels of genes related to genomic aberrations are observable in orthogonally measured high-throughput transcriptome assays. As such, simultaneous analysis of genome and transcriptome measurements should amplify important signals in the data. Motivated by this idea, we hypothesize that driver aberrations will measurably disrupt transcriptional profiles regardless of their frequency in the population.
A schematic showing how DriverNet works. (a) An example of a Cytoscape visualization of a glioblastoma patient with a high-level amplification of epidermal growth factor receptor (EGFR) (shown in green) and coincident outlying expression of genes connected to EGFR in the Reactome influence graph (shown in yellow). Examples of the overrepresented pathways (by Reactome FI plug-in for Cytoscape, FDR < 0.001) from the list of genes showing outlying expression associated with the EGFR amplification are depicted at the bottom. The box plot shows the population-level expression distribution of BRAF, an interacting protein with EGFR, and where the specific case with EGFR amplification sits on that distribution (red 'x'). We note that in this case, BRAF itself is not mutated or amplified. (b) Fitted Gaussian expression distributions of three genes that interact with EGFR: FGF11, PIK3R1, and PRKACB, with each point indicating the probability density function for individual cases. For each gene, blue dots indicate cases with mutations in the gene itself and red arrows indicate cases with outlying expression with coincident EGFR amplifications. (c) Schematic representation of the DriverNet approach. Given the genomic aberration states for different patients and genes, gene expression data, and the influence graph, which captures biological pathway information, the bipartite graph shown on the right is constructed. Green nodes on the left partition of the bipartite graph correspond to aberrated genes and nodes on the right represent the outlying expression status for each patient where red indicates outlying patient-gene events from the gene expression matrix. The genes with the highest number of outlying expression events (for example, g2) are nominated as putative drivers.
Algorithmic frameworks to exploit the relationship between genomic events and consequent changes in gene expression to nominate putative driver genes are underdeveloped. We therefore propose an integrated genome/transcriptome analysis framework, called DriverNet, to contextualize genomic aberrations (for example, mutations and copy number alterations) by their effect on transcriptional networks and identify candidate genomic aberrations suitable for functional experimental follow-up. Our approach allows individual mutations to be related to coincident changes in gene expression and assigns statistical significance to candidate predictions, thus quantitatively and rationally prioritizing candidate genes. We note that our intent differs from complementary approaches such as the one described by Vaske et al. [12], which aims at nominating driver pathways rather than driver genes in cancer, and from those that leverage genome data without considering expression [4, 13]. Both Masica and Karchin [14] and Ciriello et al. [15] integrate genome and transcriptome relationships in their framework; however, they differ from our approach, since Masica and Karchin [14] do not utilize known biological pathway information and Ciriello et al. [15] only consider mRNA expression associated with copy number aberrations and not with mutations. Other methods focusing on copy number and expression associations do not consider mutations, nor do they employ the use of previously annotated pathways [16, 17].
To study the properties and advantages of our approach, we analyzed four large-scale genome-transcriptome interrogations of tumor populations (Table 1) in human gliomas, triple negative breast cancers, a population of nearly 1,000 breast tumors (all subtypes) and high-grade serous ovarian cancers. We present results from three experiments: i) ascertainment of sensitivity and specificity in the context of several cancer datasets; ii) enumeration of well-known, but infrequent, drivers modulating transcriptional networks, and iii) identification of complex driver events that implicate compound metabolic and oncogenic pathway modulation from single genomic events.
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