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Jizu Zhi

Publications and source records attributed to Jizu Zhi.

2 recordsLinked to original sources

Proline-rich transcript of the brain (prtb) is a serum-responsive gene in osteoblasts and upregulated during adhesion.

To characterize the temporal expression of genes that play a functional role during the process of osteoblast adhesion, we used differential display (DD-PCR) on mRNA isolated from attached vs. suspended osteoblasts. A 200-bp fragment displaying upregulated expression after 30 and 60 min adhesion was isolated, sequenced, and showed 97% homology to prtb, previously showed to be expressed in mouse brain. Northern analysis confirmed a two-fold increase in prtb message during adhesion to tissue culture polystyrene, both in the presence or absence of surface-adsorbed serum proteins. Serum stimulation alone was also able to induce prtb expression, although to a lesser extent, in suspension cells. Strong prtb expression was also detected in both brain and bone of adult rats. Furthermore, prtb expression analysis during MC3T3-E1 cell differentiation revealed high expression levels independent of proliferation (day 0-7), matrix maturation (day 7-14), and mineralization (day 14-31). Time course analysis of prtb expression during adhesion of sensitized osteoblasts to serum-protein coated surfaces showed robust mRNA expression at 5 min post-plating and a peak at 10 min. The two known serum-inducible immediate early genes c-fos and c-jun showed similar expression kinetics, with c-jun mRNA levels peaking at 15 min and c-fos at 20 min. Based on these data, we hypothesize that prtb may function as an immediate early, serum-responsive, and adhesion-inducible gene with possible involvement in processes such as cell cycle control, adhesion, and proliferation.

3T3 Cells↗

Analysis techniques for microarray time-series data.

We address possible limitations of publicly available data sets of yeast gene expression. We study the predictability of known regulators via time-series analysis, and show that less than 20% of known regulatory pairs exhibit strong correlations in the Cho/Spellman data sets. By analyzing known regulatory relationships, we designed an edge detection function which identified candidate regulations with greater fidelity than standard correlation methods. We develop general methods for integrated analysis of coarse time-series data sets. These include 1) methods for automated period detection in a predominately cycling data set and 2) phase detection between phase-shifted cyclic data sets. We show how to properly correct for the problem of comparing correlation coefficients between pairs of sequences of different lengths and small alphabets. Finally, we note that the correlation coefficient of sequences over alphabets of size two can exhibit very counterintuitive behavior when compared with the Hamming distance.

Algorithms↗