Analyses of p53 target genes in the human genome by bioinformatic and microarray approaches.

The completion of the human genome sequence (International Human Genome Sequence Consortium (2001) Nature 409, 860-921; Venter, J. C., et al. (2001) Science 291, 1304-1351) allows for new ways to analyze global cellular regulatory mechanisms. Here we present a strategy to identify genes regulated by specific transcription factors in the human genome, and apply it to p53. We first collected promoters or introns of all genes available using two methods: GenBank(TM) annotation and a computationally derived transcript map. 4,852 genes analyzed in this way contained at least one p53 consensus binding sequence. Of 13 genes randomly selected for mRNA analysis, 11 were shown to respond to p53 expression. Five promoters were analyzed by chromatin immunoprecipitation, which revealed that all were bound by p53 in vivo. We then analyzed 33,615 unique human genes on cDNA microarrays, identifying 1,501 genes that respond to p53 expression. A parameter was derived that demonstrates that in silico prediction greatly enriches for genes that are activated and repressed by p53 and assists us to suggest other signaling pathways that may be connected to p53. The methods shown here illustrate a novel approach to analysis of global gene regulatory network through the integration of human genomic sequence information and genome-wide gene expression analysis.

The farnesyl transferase inhibitor SCH 66336 induces a G(2) --> M or G(1) pause in sensitive human tumor cell lines.

SCH 66336 is a potent farnesyl transferase inhibitor (FTI) in clinical development. It efficiently prevents the membrane association of H-ras, but not K- or N-ras. Yet, in soft agar, it reverts the anchorage-independent growth of human tumor cell lines (hTCLs) harboring H-ras, K-ras, and N-ras mutations, implying that blocking farnesylation of proteins besides ras may be responsible for this effect. Experiments show that SCH 66336 altered the cell cycle distribution of sensitive human tumor cells in two distinct ways. Most sensitive hTCLs accumulated in the G(2)-->M phase after the FTI treatment, but those with an activated H-ras accumulated in G(1) phase, suggesting that the biological effects induced by FTIs in cells with an activated H-ras are distinct from other sensitive cells. A careful genotypic comparison of the hTCLs revealed that those cells with wild-type p53 are especially sensitive to the FTIs. In these cells p53 and its downstream target gene p21(Cip1) are induced after treatment with SCH 66336 for 24 h. These data suggest that cell cycle effects, either G(1) or G(2)-->M accumulation, and p53 status are important for mediating the effects of FTIs on tumor cells.

Spatial patterns of hospital utilization: the impact of distance and time.

Although the impact of the physical proximity of health care facilities on utilization in rural areas is well established, its effect in metropolitan areas is still subject to question. This paper develops a spatial demand model of hospital choice to empirically estimate the impacts of distance and time on hospital utilization patterns. With a cross-product ratio estimation approach, the effects of physical access are estimated after controlling for spatial irregularities owing to the distribution of hospitals and population in metropolitan areas. The empirical results suggest that distance and time factors strongly influence hospital choice, even in metropolitan areas where alternatives are widely available, and that their effects vary across service classifications and hospitals.

Outcome of intensive care. An application of a predictive model.

The development of a scoring system to estimate equivalency of illness between individuals has been undertaken. A model has been formulated to calculate probability of survival from each of 225 potential "conditions" apt to occur in patients admitted to intensive care areas. The presence or absence of each factor was noted on admission to a university hospital ICU. The relation between conditions noted in observations on a sample of patients, and survival allows assignment of a weight to each complication on the basis of which a "Condition Index Score" (CIS) or prognosis index can be objectively calculated. Potential uses of CIS are to: (1) establish objective criteria for admission to and discharge from intensive care, and for transfer to tertiary care centers; (2) compare quality of care (outcome vs. CIS) between different intensive care facilities; (3) serve as basis for multi-institutional studies concerning critically ill patients; (4) compare outcomes in groups of patients with equal CIS who are and are not treated in ICUs; and (5) establish appropriate numbers of critical care beds for any hospital or area by CIS criteria.