Van Eyk, Dunn - Proteomic and Genomic Analysis of Cardiovascular Disease - 2003 (522919), страница 14
Текст из файла (страница 14)
A valid analogy is that we know the names inthe phone book, but certainly cannot describe the society that results from the cumulative actions of these individuals, in time, at different locations with differentcombinations of the others. Consider the hallmark of a good reporter. When s/hewants to fully describe a story so that the reader can place the information in context, the following questions must be asked and answered: who or what, why,where and how? In terms of global analyses, most experiments are simply askingwho or what. To answer the other questions, why, where and how, which is necessary to fully comprehend the processes involved, we must understand how thetranscriptome translates into the proteome and be able to place a product into itscorresponding subcellular or extracellular location, as well as identify the relevant2.5 Filtering The Transcriptome: Enhancing The Valuebiological partners.
Identifying the “why” and “how” aspects will require an exceptionally rich amount of biological annotation, placing the action(s) of the geneproduct in their spatial and temporal contexts within the biological system. Therecent successes in computational simulations of selected networks [43, 44] provides proof-of-principle for the effectiveness of using these algorithms on the cardiovascular databases.2.5Filtering The Transcriptome: Enhancing The ValueGene arrays as applied to cardiovascular disease are inherently noisy experimentsas even in a seemingly genetically homogenous population the severity and penetrance of the disease is not necessarily uniform. In order to raise the signal-tonoise ratio so that interpretable data are obtained, some form of filtering is essential.
This can occur at either the “back-end” using bioinformatics, or at the “frontend” by careful selection of the sample population (http://www.jax.org/research/churchill/research/expression/in dex.html) [45] or by sub selection of only themost relevant portion of the transcriptome (see below). Additionally, the overall experimental design is critical and details are often overlooked or not reported sothat it becomes problematic for another experimentalist to exactly repeat the procedure. Seemingly trivial matters can significantly affect a particular experiment.For example, how are the animals sacrificed? If an animal smells the blood of another in the procedure room, a stress response can be initiated.
Similarly, the circadian rhythm can have a significant impact on the hormonal status of an animal, which could impact cardiovascular status. These considerations are rarelyacted upon but could, if carefully considered, minimize the inherent biologicalnoise of the experiment.The Back-end – Very few, if any studies have documented the fulfillment of theinitial catalogue’s promise. For example, hypertrophy is a common adaptive response of the heart to increased workload, injury and stress, and has been characterized in many models as being defined by the activation of a common set ofgenes, which are normally only expressed during early cardiac development.While a number of different models of hypertrophy [46] or myocardial infarction[47] have been catalogued at the transcriptional level, the difficulty lies in translating what are essentially thousands upon thousands of observations into a coherent, testable model that can be biologically validated either through gain or loss-offunction studies.
However, one of the more carefully considered cardiovascularstudies illustrates the value of multiple comparisons and rigorous mining of theresultant data. Aronow et al. set out to examine the “monolithic” hypothesis, thatis, a gene program common to different hypertropies exists, by comparing thetranscriptomes of four genetic hypertrophy models that showed varying degrees ofthe hypertrophic response [48]. DNA microarrays were used to compare approximately 9000 mRNAs in the four transgenic mouse models. Although the totalnumber of regulated genes (defined as a certain – fold up- or down-regulated) var-35362 Cardiac Disease and The Transcriptomeied between the models with the numbers corresponding to the relative severityof the phenotypic response, no commonality between the four models could bedefined.
However, by applying a modest amount of analysis using hierarchicaltree and K-means clustering to the data sets, patterns involving 276 genes thatwere regulated among the four models could be discerned, including a subset associated with the activation of apoptosis. Northern analyses were subsequentlyused to confirm the microarray patterns and the biological annotation initiatedusing the existing databases.The Front-end – In order to enhance the signal-to-noise ratio of an experiment,its design can also apply filters at the “front-end”. For example, rather than interrogating the entire RNA complement of the cell, one can attempt to restrict itonly to those transcripts that will be processed into the proteome [49].
We recentlycarried out such a study in order to explore the efficacy of the “front-filter”approach [50]. The b-agonist, isoproterenol, when administered over a 10–14 dayperiod is a simple and well-characterized protocol that results in a characteristic20–30% cardiac hypertrophy as measured by the heart to body weight ratios [51].However, instead of interrogating the entire RNA complements of the treated anduntreated animals, only the actively translated RNA was studied. Polysomes derived from the animals’ ventricles were loaded on sucrose gradients, size fractionated using velocity density centrifugation and the RNA from these fractions usedto select for transcripts that were loaded onto polysomes in response to isoproterenol.
Four Clontech Atlas 1.2 microarray filters were simultaneously hybridized toradiolabeled cDNA probes derived from either vehicle-treated (control) free orpolysome bound RNA, or from isoproterenol-treated free or polysome boundRNA. A numerical value for the shift to polysomes was calculated by taking theratio of isoproterenol bound/free signal divided by the ratio of vehicle-treatedbound/free signal. Signals were normalized to a median filter value to correct fordifferences in probe specific activities with increases of a particular transcript inthe polysome fraction due to chronic isoproterenol infusion resulting in a value of> 1.0. Thus, while a particular transcript’s steady state level might not be increasedduring the treatment, if its translational efficiency was affected, it would be detected.
This high-throughput screen, designed to identify only the transcripts thatare actively translated during cardiac hypertrophy or whose translation is downregulated during the physiological response, identified a number of genes with established links to hypertrophy, including Sp3, c-jun, annexin II, cathepsin B, andHB-EGF [52–56], confirming the screen’s accuracy.
However, in order to test theusefulness of the screen, we decided to focus on a candidate transcript that hadnot been previously linked to hypertrophy and found that protein levels of the tumor suppressor PTEN (phosphatase and tensin homologue on chromosome ten)were increased in the absence of increased messenger RNA levels (Fig. 2.1). Whileoverall, the mRNA levels of PTEN were not increased as result of isoproterenoltreatment, the movement of the existing transcripts into the heavy portion of thepolysomes was. Quantitative western blot analyses showed that PTEN protein expression is, in fact, induced in isoproterenol-treated mouse hearts relative to vehicle-treated hearts [50], in agreement with the polysome-derived data.
Taken to-2.5 Filtering The Transcriptome: Enhancing The ValuePolysome-derived RNA levels changealthough total RNA amounts remain stable.For each array position that had a signalgreater than 0.5X the median filter value aswell as a signal on all membranes, the ratio(TI/UI)/(TS/US), where: US, untranslatedsham (vehicle solvent only); TS, translatedsham; UI, untranslated isoproterenol treated;TI, translated isoproterenol treated. A ratio >1indicates a shift toward polysomes in the isoproterenol treated hearts and <1 indicates ashift away from polysomes.
Only candidatesthat had a ratio of >2 were selected. DRNA.Fig. 2.1Total RNA changes were calculated by summing signals from all four array membranes.With the exception of the serine/threoninekinase, pim-1 (gene #23), the candidategenes exhibited minimal RNA fluctuation.DPolysomes. Significant changes occurred inthe degree of polysome loading (movementto the heavy fraction). PTEN was selected onthe basis of the subsequent, biological annotation.
Note that the constitutive markers,GAPDH and alpha actin, remained relativelyunchanged.gether with the shift of PTEN mRNA into the heavy polysome fractions duringhypertrophy and the minimal change of total PTEN mRNA, this finding is consistent with regulation of PTEN expression by increased translational initiation.PTEN was originally identified as a human tumor-suppressor gene and is alsocalled MMAC1/TEP1 (MMAC1, mutated in multiple advanced cancers-1; TEP1,TGF-b regulated, epithelial cell enriched phosphatase). The gene is either deletedor inactivated in a high percentage of breast, endometrial, brain and prostate cancers [57–59].
![](https://s.studizba.com/z.php?f=/templates/si/i/noavatar.png&w=100&h=100&t=1"){kind=avatar}
