Jatin Misra

Genome-Wide Dynamic Transcriptional Profiling of the Light-to-Dark Transition in Synechocystis sp. Strain PCC 6803

We report the results of whole-genome transcriptional profiling of the light-to-dark transition with the model photosynthetic prokaryote Synechocystis sp. strain PCC 6803 (Synechocystis). Experiments were conducted by growing Synechocystis cultures to mid-exponential phase and then exposing them to two cycles of light/dark conditions, during which RNA samples were obtained. These samples were probed with a full-genome DNA microarray (3,169 genes, 20 samples) as well as a partial-genome microarray (88 genes, 29 samples). We concluded that (i) 30-min sampling intervals accurately captured transcriptional dynamics throughout the light/dark transition, (ii) 25% of the Synechocystis genes (783 genes) responded positively to the presence of light, and (iii) the response dynamics varied greatly for individual genes, with a delay of up to 120 to 150 min for some genes. Four classes of genes were identified on the basis of their dynamic gene expression profiles: class I (108 genes, 30-min response time), class II (279 genes, 60 to 90 min), class III (258 genes, 120 to 150 min), and class IV (138 genes, 180 min). The dynamics of several transcripts from genes involved in photosynthesis and primary energy generation are discussed. Finally, we applied Fisher discriminant analysis to better visualize the progression of the overall transcriptional program throughout the light/dark transition and to determine those genes most indicative of the lighting conditions during growth.

Mapping physiological states from microarray expression measurements

MOTIVATION The increasing use of DNA microarrays to probe cell physiology requires methods for visualizing different expression phenotypes and explicitly connecting individual genes to discriminating expression features. Such methods should be robust and maintain biological interpretability. RESULTS We propose a method for the mapping of the physiological state of cells and tissues from multidimensional expression data such as those obtained with DNA microarrays. The method uses Fisher discriminant analysis to create a linear projection of gene expression measurements that maximizes the separation of different sample classes. Relative to other typical classification methods, this method provides insights into the discriminating characteristics of expression measurements in terms of the contribution of individual genes to the definition of distinct physiological states. This projection method also facilitates visualization of classification results in a reduced dimensional space. Examples from four different cases demonstrate the ability of the method to produce well-separated groups in the projection space and to identify important genes for defining physiological states. The method can be augmented to also include data from the proteomic and metabolic phenotypes and can be useful in disease diagnosis, drug screening and bioprocessing applications.

Linking hepatic transcriptional changes to high-fat diet induced physiology for diabetes-prone and obese-resistant mice.

Insulin resistance is characterized by high insulin levels and decreased responsiveness of tissues to the clearance of glucose from the bloodstream. This study maintained the diabetes-prone C57BL/6J and obese-resistant A/J mice strains on a high-fat diet for 12 weeks to transcriptionally profile the liver for changes caused by high fat diet. In the 8th week of the experiment, the C57BL/6J mice began exhibiting signs of insulin resistance, while the A/J mice did not show any such indications during the course of the experiment. A regression model of partial least squares between serum insulin measurements and the liver gene expression profile for the C57BL/6J mice on a high-fat diet was constructed in an effort to quantitatively link the physiological measurement with the gene expressions. A series of discriminating genes between high fat and chow fed mice was generated for both the C57BL/6J and A/J strains. These discriminatory genes contain information about the mechanisms responsible for the development of insulin resistance, and the compensation for a high fat diet, respectively. The results identified several genes involved in the development of insulin resistance and serve as a framework for other studies involving other organs affected by this systemic disease.

Functional genomics and systems engineering

Abstract The systematic elucidation of biological phenomena and the development of effective biotechnological solutions to a series of commercial problems are both increasingly dependent on systems engineering approaches to handle the effective analysis of rapidly accumulating amounts of data on the genome of various species, and the monitoring of biological quantities. such as mRNAs. proteins. and metabolites. In this paper we discuss the general framework of such problems and provide an outline of the systems engineering approaches that promise to address the above issues.