ABSTRACT
Heat shock proteins and molecular chaperones are key components contributing to survival in the abiotic stress response. Porphyra seriata grows on intertidal rocks exposed to dynamic environmental changes associated with the turning tides, including desiccation and heat stress. Analysis of the ESTs of P. seriata allows us to identify the nine HSP cDNAs, which are predicted to be PsHSP90, three PsHSP70, PsHSP40 and PsHSP20, and three 5'-truncated HSP cDNAs. RT-PCR results show that most of the PsHSP transcripts were detected under normal cell growth conditions as well as heat stress, with the exception of two cDNAs. In particular, PsHSP70b and PsHSP20 transcripts were upregulated by heat stress. When the putative mitochondrial PsHSP70b was introduced and overexpressed in Chlamydomonas, transformed Chlamydomonas evidenced higher rates of survival and growth than those of the wild type under heat stress conditions. Constitutive overexpression of the PsHSP70b gene increases the transcription of the HSF1 as well as the CrHSP20 and CrHSP70 gene. These results indicate that PsHSP70b is involved in tolerance to heat stress and the effects on transcription of the CrHSP20 and CrHSP70 genes.
Subject(s)
Acclimatization/physiology , HSP70 Heat-Shock Proteins/metabolism , Heat-Shock Response/physiology , Plant Proteins/metabolism , Porphyra/classification , Porphyra/metabolismABSTRACT
Temperature is one of the major environmental factors that affect the distribution, growth rate, and life cycle of intertidal organisms, including red algae. In an effort to identify the genes involved in the high-temperature tolerance of Porphyra, we generated 3,979 expression sequence tags (ESTs) from gametophyte thalli of P. seriata Kjellm. under normal growth conditions and high-temperature conditions. A comparison of the ESTs from two cDNA libraries allowed us to identify the high temperature response (HTR) genes, which are induced or up-regulated as the result of high-temperature treatment. Among the HTRs, HTR2 encodes for a small polypeptide consisting of 144 amino acids, which is a noble nuclear protein. Chlamydomonas expressing the Porphyra HTR2 gene shows higher survival and growth rates than the wild-type strain after high-temperature treatment. These results suggest that HTR2 may be relevant to the tolerance of high-temperature stress conditions, and this Porphyra EST data set will provide important genetic information for studies of the molecular basis of high-temperature tolerance in marine algae, as well as in Porphyra.
ABSTRACT
PURPOSE: One of the main challenges of lung cancer research is identifying patients at high risk for recurrence after surgical resection. Simple, accurate, and reproducible methods of evaluating individual risks of recurrence are needed. EXPERIMENTAL DESIGN: Based on a combined analysis of time-to-recurrence data, censoring information, and microarray data from a set of 138 patients, we selected statistically significant genes thought to be predictive of disease recurrence. The number of genes was further reduced by eliminating those whose expression levels were not reproducible by real-time quantitative PCR. Within these variables, a recurrence prediction model was constructed using Cox proportional hazard regression and validated via two independent cohorts (n = 56 and n = 59). RESULTS: After performing a log-rank test of the microarray data and successively selecting genes based on real-time quantitative PCR analysis, the most significant 18 genes had P values of <0.05. After subsequent stepwise variable selection based on gene expression information and clinical variables, the recurrence prediction model consisted of six genes (CALB1, MMP7, SLC1A7, GSTA1, CCL19, and IFI44). Two pathologic variables, pStage and cellular differentiation, were developed. Validation by two independent cohorts confirmed that the proposed model is significantly accurate (P = 0.0314 and 0.0305, respectively). The predicted median recurrence-free survival times for each patient correlated well with the actual data. CONCLUSIONS: We have developed an accurate, technically simple, and reproducible method for predicting individual recurrence risks. This model would potentially be useful in developing customized strategies for managing lung cancer.
