Van Eyk, Dunn - Proteomic and Genomic Analysis of Cardiovascular Disease - 2003 (522919), страница 25
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Genes are initially divided into a number (k) of user-defined andequally-sized groups. Centroids are calculated for each group corresponding tothe average of the expression profiles. Individual genes are then reassigned tothe group in which the centroid is the most similar to the gene. Group centroidsare then recalculated, and the process is iterated until the group compositions converge. A wide selection of similarity measures (parametric and non-parametriccorrelations, Euclidean distance, etc.) is available in different software.· Self-Organizing Maps (SOMs) are tools for exploring and mapping the variations inexpression patterns within an experiment. This method is similar to k-means clustering, but with an additional feature where the resulting groups of genes can bedisplayed in a rectangular pattern, with distance representing the level of similarity (adjacent groups being more similar than groups further away).· Principal components analysis (PCA) is standard protection technique that explores the variability in gene expression patterns and finds a small number ofthemes in expression pattern.
These themes can be combined to make all thedifferent gene expression patterns in a data set.· Multidimentional Scaling (MDS) is a method that represents the measure ofsimilarity between pairs of objects. In the clustering section distance matrix istypically used as a similarity matrix between all pairs of samples in one experiment design. Two dimensional scaling plots are used to examine the similarityamongst all samples.There is no software so far that can extract all the useful data and prevent possible masking of some clusters by transcriptional “noise”.
Software has becomemuch more powerful in the past few years, but expert data-miners and bioinformaticians are still needed.4.12Application: New Classification of DiseaseSystem wide explorations of gene expression patterns provide a unique insightinto the internal environment of the cells of a particular organ. This may reflectboth genetic predisposition towards the disease, and environmental stresses thatlead to the disease phenotype. Heart failure is a classic condition in which diversestimuli may dissimilarly and/or similarly challenge the ability of the heart toadapt [28]. When adaptation becomes limited, disease phenotypes evolve.
Indeed,we have traditionally classified heart failure in terms of clinical etiology, for exam"The ability for microarray expressiondata to distinguish the gene expression pattern of normal (N), and ischemic cardiomyopathy (ICM) can be seen from this hierarchical clustering and dendrogram display. TheFig. 4.5genes up and down regulated include bothknown genes as well as completely novelgenes of currently unknown function. Thismay provide both diagnostic and prognosticinformation in the future.4.12 Application: New Classification of Disease73744 cDNA Microarrays in Heart Failure Researchple, dilated cardiomyopathy, ischemic cardiomyopathy, viral myocarditis, etc.
Weassume also that the disease progression, the prognosis and response to therapywill be predicated on the disease etiology. However, the ability of microarrays toprovide a broad insight into the disease process directly within the tissues providea unique insight into the intracellular perturbations of the cell organization andfunction (Fig. 4.5) and an entirely unique new perspective on the heart failure process. Commonalities and differences at the molecular level will identify criticalpathways of pathogenesis and/or response to therapy.This approach have been very successfully applied to the field of cancer biology.In the study of breast cancer, expression microarrays have provided an importantinsight into the biology of the disease, as well as prognostic markers for favourable vs.
poor outcomes. Such methods have also been applied to leukemias, aswell as prostate cancer (Fig. 4.6). The advantage in cancer biology is that the tumour is often excised, providing a direct source of tissue to correlate with pathology, as well as opportunity to explore patterns of pathway activation. However,The pattern of expression microarrays characterizes the biology of the tissues.This is best illustrated in cases of cancer,where the expression patterns between different types of tumours (e.g. lymphoma) notFig. 4.6only can differentiate one type of lymphomafrom another, but also can be associated withdifferential prognosis. This will likely becomemore refined as the database becomes moreenriched with samples in time.4.14 Application: Early Disease Markers and Prognosiswith the availability of myocardial biopsies, similar opportunities exist for thestudy of heart failure.4.13Application: Pathogenesis of DiseaseDespite its direct relevance, the direct evaluation of human heart failure samplesusing microarray technology also has significant limitations.
The most obvious isthe end stage nature of the samples, which may represent a convergence of phenotypes that have no relevance to the pathogenetic mechanisms. Furthermore,many of these patients also have co-morbidities and concomitant medications,which all serve to skew the gene expression patterns.
Nevertheless, clinical validation in a patient population is always important to ensure relevance and concordance amongst biological models.To obviate these concerns, and to capture the potential early triggers of theheart failure phenotype, animal models of heart failure have been used to give interesting insights. Taylor et al. examined the viral model of myocarditis, and identified specific groups of genes relevant to the viral, inflammatory and healingphases of the myocarditic process [29].
Aronow et al. showed that no single geneprogram is common to all the models of heart failure. However, there does appear to be a group of programs, linked specifically to each etiology, that predisposes to heart failure [30]. In addition, we have performed serial microarray analysis of gene families potentially relevant in the setting of heart failure in animalmodels of heart failure. By using carefully controlled experimental designs, wherethe animals subjected to the same injury can be synchronously followed and analyzed, and compared to age matched controls, the critical differences in host responses leading to heart failure can be precisely identified (Figs. 4.7, 4.8).
In thefuture, the applications of these concepts to either biopsy or preferentially bloodbased analysis would be of most important interest in studies of heart failure.4.14Application: Early Disease Markers and PrognosisTo identify disease early in its process, critical specific markers will be useful inthe diagnoses and in delineating desease etiology. Currently, the best example ofan early diagnostic marker is brain natriuretic peptide (BNP), which is elaboratedby the ventricular myocardium under stress.
The spill-over of this marker into theblood has given a useful marker in early diagnosis of heart failure, and also provides prognostic information. However, it is elevated irrespective of heart failureetiology. Thus we must ask whether there are etiologically specific marker thatcan be found. We have recently identified 5 potentially useful markers for theearly diagnosis of myocarditis leading to dilated cardiomyopathy (unpublished observations).
This may indeed represent the beginning for future applications of75The expression array information can be functionallyclustered to provide biological insight into disease processes, withthe brightness of the colour of each molecule representing therelative levels of expression. For example, comparing heart tissuesfrom models of heart failure with that of normal condition, we seeFig.
4.7that Cdc25a and Cdk4 are significantly up regulated, while Cdk2 issignificantly down regulated. This pattern suggests that there isincreased propensity towards cell cycle progression, but ultimatelyis blocked.764 cDNA Microarrays in Heart Failure ResearchFig. 4.8Expression arrays are also useful to determine host stressresponse signaling pathways in the presence of heart failure. In thisexperiment, we can see that intracellular cytokine signaling path-Normalways of lck and fyn are both up regulated in the setting of heartfailure.
This is consistent with the activation of cytokine and immune signaling pathways in the setting of heart failure.Heart FailureCell Stress Response4.14 Application: Early Disease Markers and Prognosis77784 cDNA Microarrays in Heart Failure Researchthis technology to systematically identify novel ethology specific markers of disease.This strategy has also been recently employed in prostatic cancer, where earlyand accurate diagnosis is particularly useful for determining course of action.Using microarray methods, an early diagnostic pattern was indeed recognized,two signaling molecules appear to be unique to early prostatic cancer – hepsinand pim-1, both are serine-threonine kinases [31].In addition, with the increasing utilization of ventricular assist devices, there isaccess to tissues from heart failure patients has improved.
In addition, gatheringdata has now become available on the potential changes and reversibility of theabnormalities observed in heart failure, and the opportunity of using this information in the future for prognosis of the patients, and in turn determining which patient may come off the assist device support, and which patient will need hearttransplantation.4.15Application: Therapeutic InsightsHow a drug acomplishes its therapeutic effect in the clinical setting has alwaysbeen difficult. A drug may be developed for the purpose of targeting a specificpathway. However, in many occasions, it is the unintended effects of a drug thatultimately determine its overall biological profile in the clinical setting.
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