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Expression variation: its relevance to emergence of chronic disease and to therapy.

Mayburd AL - PLoS ONE (2009)

Bottom Line: Anti-cancer FDA approved targets were displaying much higher variability as a class compared to random genes.Study of variability profiles may lead to novel diagnostic methods, therapies and better drug target prioritization.The results of the study suggest the need to advance personalized therapy development.

View Article: PubMed Central - PubMed

Affiliation: CPA Global, Alexandria, VA, USA. AMayburd@cpaglobal.com

ABSTRACT

Background: Stochastic fluctuations in the protein turnover underlie the random emergence of neural precursor cells from initially homogenous cell population. If stochastic alteration of the levels in signal transduction networks is sufficient to spontaneously alter a phenotype, can it cause a sporadic chronic disease as well -- including cancer?

Methods: Expression in >80 disease-free tissue environments was measured using Affymetrix microarray platform comprising 54675 probe-sets. Steps were taken to suppress the technical noise inherent to microarray experiment. Next, the integrated expression and expression variability data were aligned with the mechanistic data covering major human chronic diseases.

Results: Measured as class average, variability of expression of disease associated genes measured in health was higher than variability of random genes for all chronic pathologies. Anti-cancer FDA approved targets were displaying much higher variability as a class compared to random genes. Same held for magnitude of gene expression. The genes known to participate in multiple chronic disorders demonstrated the highest variability. Disease-related gene categories displayed on average more intricate regulation of biological function vs random reference, were enriched in adaptive and transient functions as well as positive feedback relationships.

Conclusions: A possible causative link can be suggested between normal (healthy) state gene expression variation and inception of major human pathologies, including cancer. Study of variability profiles may lead to novel diagnostic methods, therapies and better drug target prioritization. The results of the study suggest the need to advance personalized therapy development.

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Related in: MedlinePlus

Functional analysis of gene categories displaying opposing extremes of expression variability.Comparative results of key-word searching of the most and least disease-related functional categories. The 7500 functional categories produced by AMIGO ontological classification were filtered resulting in ∼3900 with non-zero population. Multiple randomly drawn sets of genes (500-1000 in size) served as negative control. The functional enrichment coefficients (FENR) were computed in the AMIGO-represented negative control and similarly treated disease-related datasets. The strings of FENR formed random and disease-related sub-profiles in each functional category. The sub-profiles were compared by T-test and p-values were sorted. The functional categories with the least p-values (best 10% of rank, p<10–11) were termed “most disease-related” (black bars). The functional categories with the highest p-values (>0.9) were termed “least disease-related” (striped bars). Grey bars stand for the total population of AMIGO-derived functions. The most and the least disease-related groups of functional categories were searched using the keyword combinations, such as “regulation”, “positive regulation” and “negative regulation”. The fractions of the functions responding to the keyword combinations were computed and plotted.
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pone-0005921-g007: Functional analysis of gene categories displaying opposing extremes of expression variability.Comparative results of key-word searching of the most and least disease-related functional categories. The 7500 functional categories produced by AMIGO ontological classification were filtered resulting in ∼3900 with non-zero population. Multiple randomly drawn sets of genes (500-1000 in size) served as negative control. The functional enrichment coefficients (FENR) were computed in the AMIGO-represented negative control and similarly treated disease-related datasets. The strings of FENR formed random and disease-related sub-profiles in each functional category. The sub-profiles were compared by T-test and p-values were sorted. The functional categories with the least p-values (best 10% of rank, p<10–11) were termed “most disease-related” (black bars). The functional categories with the highest p-values (>0.9) were termed “least disease-related” (striped bars). Grey bars stand for the total population of AMIGO-derived functions. The most and the least disease-related groups of functional categories were searched using the keyword combinations, such as “regulation”, “positive regulation” and “negative regulation”. The fractions of the functions responding to the keyword combinations were computed and plotted.

Mentions: The FENR values were assembled in the panel, with two sub-profiles in every populated functional category, one for random negative control and another for the diseases being grouped together. Such grouping allowed exploration of the features generic to all chronic disorders using the rationale presented in Methods. The functional categories ranked based on p-value of T-test vs. random negative control were subjected to text-mining, as well as the total list of categories. The results are given by Figure 7.


Expression variation: its relevance to emergence of chronic disease and to therapy.

Mayburd AL - PLoS ONE (2009)

Functional analysis of gene categories displaying opposing extremes of expression variability.Comparative results of key-word searching of the most and least disease-related functional categories. The 7500 functional categories produced by AMIGO ontological classification were filtered resulting in ∼3900 with non-zero population. Multiple randomly drawn sets of genes (500-1000 in size) served as negative control. The functional enrichment coefficients (FENR) were computed in the AMIGO-represented negative control and similarly treated disease-related datasets. The strings of FENR formed random and disease-related sub-profiles in each functional category. The sub-profiles were compared by T-test and p-values were sorted. The functional categories with the least p-values (best 10% of rank, p<10–11) were termed “most disease-related” (black bars). The functional categories with the highest p-values (>0.9) were termed “least disease-related” (striped bars). Grey bars stand for the total population of AMIGO-derived functions. The most and the least disease-related groups of functional categories were searched using the keyword combinations, such as “regulation”, “positive regulation” and “negative regulation”. The fractions of the functions responding to the keyword combinations were computed and plotted.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2692004&req=5

pone-0005921-g007: Functional analysis of gene categories displaying opposing extremes of expression variability.Comparative results of key-word searching of the most and least disease-related functional categories. The 7500 functional categories produced by AMIGO ontological classification were filtered resulting in ∼3900 with non-zero population. Multiple randomly drawn sets of genes (500-1000 in size) served as negative control. The functional enrichment coefficients (FENR) were computed in the AMIGO-represented negative control and similarly treated disease-related datasets. The strings of FENR formed random and disease-related sub-profiles in each functional category. The sub-profiles were compared by T-test and p-values were sorted. The functional categories with the least p-values (best 10% of rank, p<10–11) were termed “most disease-related” (black bars). The functional categories with the highest p-values (>0.9) were termed “least disease-related” (striped bars). Grey bars stand for the total population of AMIGO-derived functions. The most and the least disease-related groups of functional categories were searched using the keyword combinations, such as “regulation”, “positive regulation” and “negative regulation”. The fractions of the functions responding to the keyword combinations were computed and plotted.
Mentions: The FENR values were assembled in the panel, with two sub-profiles in every populated functional category, one for random negative control and another for the diseases being grouped together. Such grouping allowed exploration of the features generic to all chronic disorders using the rationale presented in Methods. The functional categories ranked based on p-value of T-test vs. random negative control were subjected to text-mining, as well as the total list of categories. The results are given by Figure 7.

Bottom Line: Anti-cancer FDA approved targets were displaying much higher variability as a class compared to random genes.Study of variability profiles may lead to novel diagnostic methods, therapies and better drug target prioritization.The results of the study suggest the need to advance personalized therapy development.

View Article: PubMed Central - PubMed

Affiliation: CPA Global, Alexandria, VA, USA. AMayburd@cpaglobal.com

ABSTRACT

Background: Stochastic fluctuations in the protein turnover underlie the random emergence of neural precursor cells from initially homogenous cell population. If stochastic alteration of the levels in signal transduction networks is sufficient to spontaneously alter a phenotype, can it cause a sporadic chronic disease as well -- including cancer?

Methods: Expression in >80 disease-free tissue environments was measured using Affymetrix microarray platform comprising 54675 probe-sets. Steps were taken to suppress the technical noise inherent to microarray experiment. Next, the integrated expression and expression variability data were aligned with the mechanistic data covering major human chronic diseases.

Results: Measured as class average, variability of expression of disease associated genes measured in health was higher than variability of random genes for all chronic pathologies. Anti-cancer FDA approved targets were displaying much higher variability as a class compared to random genes. Same held for magnitude of gene expression. The genes known to participate in multiple chronic disorders demonstrated the highest variability. Disease-related gene categories displayed on average more intricate regulation of biological function vs random reference, were enriched in adaptive and transient functions as well as positive feedback relationships.

Conclusions: A possible causative link can be suggested between normal (healthy) state gene expression variation and inception of major human pathologies, including cancer. Study of variability profiles may lead to novel diagnostic methods, therapies and better drug target prioritization. The results of the study suggest the need to advance personalized therapy development.

Show MeSH
Related in: MedlinePlus