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Biomedical subjects

Yoganand Balagurunathan

Publications and source records attributed to Yoganand Balagurunathan.

4 recordsLinked to original sources

Epigenetic transdifferentiation of normal melanocytes by a metastatic melanoma microenvironment.

The clinical management of cutaneous melanoma would benefit significantly from a better understanding of the molecular changes that occur during melanocytic progression to a melanoma phenotype. To gain unique insights into this process, we developed a three-dimensional in vitro model that allows observations of normal human melanocytes interacting with a metastatic melanoma matrix to determine whether these normal cells could be reprogrammed by inductive cues in the tumor cell microenvironment. The results show the epigenetic transdifferentiation of the normal melanocytic phenotype to that of an aggressive melanoma-like cell with commensurate increased migratory and invasive ability with no detectable genomic alterations. Removal of the transdifferentiated melanocytes from the inductive metastatic melanoma microenvironment results in a reversion to their normal phenotype. However, a normal melanocyte microenvironment had no epigenetic influence on the phenotype of metastatic melanoma cells. This novel approach identifies specific genes involved in the transdifferentiation of melanocytes to a more aggressive phenotype, which may offer significant therapeutic value.

Cell Differentiation↗

Unraveling gene-gene interactions regulated by ligands of the aryl hydrocarbon receptor.

The co-expression of genes coupled to additive probabilistic relationships was used to identify gene sets predictive of the complex biological interactions regulated by ligands of the aryl hydrocarbon receptor ((Italic)Ahr(/Italic)). To maximize the number of possible gene-gene combinations, data sets from murine embryonic kidney, fetal heart, and vascular smooth muscle cells challenged (Italic)in vitro(/Italic) with ligands of the (Italic)Ahr(/Italic) were used to create predictor/training data sets. Biologically relevant gene predictor sets were calculated for (Italic)Ahr(/Italic), cytochrome P450 1B1, insulin-like growth factor-binding protein-5, lysyl oxidase, and osteopontin. Transcript levels were categorized into ternary expressions and target genes selected from the data set and tested for all possible combinations using three gene sets as predictors of transitional level. The goodness of prediction for each set was quantified using a multivariate nonlinear coefficient of determination. Evidence is presented that predictor gene combinations can be effectively used to resolve gene-gene interactions regulated by (Italic)Ahr(/Italic) ligands. (Italic)Key words:(/Italic) aryl hydrocarbon receptor, bioinformatics, gene networks, genomics. (Italic)Environ Health Perspect (/Italic)112:403-412 (2004). [Online 14 January 2004]

Animals↗

Simulation of cDNA microarrays via a parameterized random signal model.

cDNA microarrays provide simultaneous expression measurements for thousands of genes that are the result of processing images to recover the average signal intensity from a spot composed of pixels covering the area upon which the cDNA detector has been put down. The accuracy of the signal measurement depends on using an appropriate algorithm to process the images. This includes determining spot locations and processing the data in such a way as to take into account spot geometry, background noise, and various kinds of noise that degrade the signal. This paper presents a stochastic model for microarray images. There are over 20 model parameters, each governed by a probability distribution, that control the signal intensity, spot geometry, spot drift, background effects, and the many kinds of noise that affect microarray images owing to the manner in which they are formed. The model can be used to analyze the performance of image algorithms designed to measure the true signal intensity because the ground truth (signal intensity) for each spot is known. The levels of foreground noise, background noise, and spot distortion can be set, and algorithms can be evaluated under varying conditions.

Algorithms↗

Noise factor analysis for cDNA microarrays.

A microarray-image model is used that takes into account many factors, including spot morphology, signal strength, background fluorescent noise, and shape and surface degradation. The model yields synthetic images whose appearance and quality reflect that of real microarray images. The model is used to link noise factors to the fidelity of signal extraction with respect to a standard image-extraction algorithm. Of particular interest is the identification of the noise factors and their interactions that significantly degrade the ability to accurately detect the true gene-expression signal. This study uses statistical criteria in conjunction with the simulation of various noise conditions to better understand the noise influence on signal extraction for cDNA microarray images. It proposes a paradigm that is implemented in software. It specifically considers certain kinds of noise in the noise model and sets these at certain levels; however, one can choose other types of noise or use different noise levels. In sum, it develops a statistical package that can work in conjunction with the existing image simulation toolbox.

Algorithms↗