The design, computer modeling, solution structure, and biological evaluation of synthetic analogs of bryostatin 1

The bryostatins are a unique family of emerging cancer chemotherapeutic candidates isolated from marine bryozoa. Although the biochemical basis for their therapeutic activity is not known, these macrolactones exhibit high affinities for protein kinase C (PKC) isozymes, compete for the phorbol ester binding site on PKC, and stimulate kinase activity in vitro and in vivo. Unlike the phorbol esters, they are not first-stage tumor promoters. The design, computer modeling, NMR solution structure, PKC binding, and functional assays of a unique class of synthetic bryostatin analogs are described. These analogs (7b, 7c, and 8) retain the putative recognition domain of the bryostatins but are simplified through deletions and modifications in the C4-C14 spacer domain. Computer modeling of an analog prototype (7a) indicates that it exists preferentially in two distinct conformational classes, one in close agreement with the crystal structure of bryostatin 1. The solution structure of synthetic analog 7c was determined by NMR spectroscopy and found to be very similar to the previously reported structures of bryostatins 1 and 10. Analogs 7b, 7c, and 8 bound strongly to PKC isozymes with Ki = 297, 3.4, and 8.3 nM, respectively. Control 7d, like the corresponding bryostatin derivative, exhibited weak PKC affinity, as did the derivative, 9, lacking the spacer domain. Like bryostatin, acetal 7c exhibited significant levels of in vitro growth inhibitory activity (1.8–170 ng/ml) against several human cancer cell lines, providing an important step toward the development of simplified, synthetically accessible analogs of the bryostatins.

Dynamic modeling of gene expression data

We describe the time evolution of gene expression levels by using a time translational matrix to predict future expression levels of genes based on their expression levels at some initial time. We deduce the time translational matrix for previously published DNA microarray gene expression data sets by modeling them within a linear framework by using the characteristic modes obtained by singular value decomposition. The resulting time translation matrix provides a measure of the relationships among the modes and governs their time evolution. We show that a truncated matrix linking just a few modes is a good approximation of the full time translation matrix. This finding suggests that the number of essential connections among the genes is small.

Statistical modeling of large microarray data sets to identify stimulus-response profiles

A statistical modeling approach is proposed for use in searching large microarray data sets for genes that have a transcriptional response to a stimulus. The approach is unrestricted with respect to the timing, magnitude or duration of the response, or the overall abundance of the transcript. The statistical model makes an accommodation for systematic heterogeneity in expression levels. Corresponding data analyses provide gene-specific information, and the approach provides a means for evaluating the statistical significance of such information. To illustrate this strategy we have derived a model to depict the profile expected for a periodically transcribed gene and used it to look for budding yeast transcripts that adhere to this profile. Using objective criteria, this method identifies 81% of the known periodic transcripts and 1,088 genes, which show significant periodicity in at least one of the three data sets analyzed. However, only one-quarter of these genes show significant oscillations in at least two data sets and can be classified as periodic with high confidence. The method provides estimates of the mean activation and deactivation times, induced and basal expression levels, and statistical measures of the precision of these estimates for each periodic transcript.

Blood flow and oxygen delivery to human brain during functional activity: Theoretical modeling and experimental data

Coupling of cerebral blood flow (CBF) and cerebral metabolic rate for oxygen (CMRO2) in physiologically activated brain states remains the subject of debates. Recently it was suggested that CBF is tightly coupled to oxidative metabolism in a nonlinear fashion. As part of this hypothesis, mathematical models of oxygen delivery to the brain have been described in which disproportionately large increases in CBF are necessary to sustain even small increases in CMRO2 during activation. We have explored the coupling of CBF and oxygen delivery by using two complementary methods. First, a more complex mathematical model was tested that differs from those recently described in that no assumptions were made regarding tissue oxygen level. Second, [15O] water CBF positron emission tomography (PET) studies in nine healthy subjects were conducted during states of visual activation and hypoxia to examine the relationship of CBF and oxygen delivery. In contrast to previous reports, our model showed adequate tissue levels of oxygen could be maintained without the need for increased CBF or oxygen delivery. Similarly, the PET studies demonstrated that the regional increase in CBF during visual activation was not affected by hypoxia. These findings strongly indicate that the increase in CBF associated with physiological activation is regulated by factors other than local requirements in oxygen.